Pub Date : 2024-10-01DOI: 10.1016/j.lanhl.2024.08.004
Prof Penny Rapaport PhD , Sarah Amador PhD , Mariam O Adeleke PhD , Prof Julie A Barber PhD , Prof Sube Banerjee MD , Prof Georgina Charlesworth PhD , Chris Clarke DClinPsy , Prof Colin A Espie DSc , Lina Gonzalez MSc , Rossana Horsley GradCert , Prof Rachael Hunter MSc , Prof Simon D Kyle PhD , Monica Manela BM , Malgorzata Raczek PhD , Prof Zuzana Walker MD , Lucy Webster PhD , Hang Yuan PhD , Prof Gill Livingston MD
Background
Sleep disturbances are common and distressing for people with dementia and their families. Non-pharmacological interventions should be first-line management, avoiding harmful pharmacological side-effects, but there is none with known effectiveness. We aimed to establish whether DREAMS START, a multicomponent intervention, reduced sleep disturbance in people with dementia living at home compared with usual care.
Methods
We conducted a phase 3, two-arm, multicentre, parallel-arm, superiority randomised controlled trial with masked outcome assessment, recruiting dyads of people with dementia and sleep disturbance and family carers from community settings. Randomisation to the DREAMS START intervention (plus usual treatment) or usual treatment was conducted at dyadic level, blocked, and stratified by site, with a web-based system assigning allocation. DREAMS START is a six-session, manualised intervention delivered face to face or remotely by non-clinically trained graduates over an approximately 3-month period. The primary outcome was sleep disturbance measured by the Sleep Disorders Inventory (SDI) at 8 months. Analyses were on the intention-to-treat population. This trial is registered with ISRCTN 13072268.
Findings
Between Feb 24, 2021, and March 5, 2023, 377 dyads were randomly assigned (1:1), 189 to usual treatment and 188 to intervention. The mean age of participants with dementia was 79·4 years (SD 9·0), and 206 (55%) were women. The mean SDI score at 8 months was lower in the intervention group compared with the usual treatment group (15·16 [SD 12·77], n=159, vs 20·34 [16·67], n=163]; adjusted difference in means –4·70 [95% CI –7·65 to –1·74], p=0·002). 17 (9%) people with dementia in the intervention group and 17 (9%) in the control group died during the trial; the deaths were unrelated to the intervention.
Interpretation
To our knowledge, DREAMS START is the first multicomponent intervention to improve the sleep of people living at home with dementia more than usual clinical care. It had sustained effectiveness beyond intervention delivery. The intervention’s delivery by non-clinically trained graduates increases the potential for implementation within health services, adding to usual clinical care.
Funding
National Institute for Health and Care Research Health Technology Assessment.
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Pub Date : 2024-10-01DOI: 10.1016/S2666-7568(24)00109-0
Ruiyang Li , Haotian Lin
{"title":"A retinal biomarker of biological age based on composite clinical phenotypic information","authors":"Ruiyang Li , Haotian Lin","doi":"10.1016/S2666-7568(24)00109-0","DOIUrl":"10.1016/S2666-7568(24)00109-0","url":null,"abstract":"","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 10","pages":"Article 100603"},"PeriodicalIF":13.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.lanhl.2024.07.013
Prof Michael Schöll PhD , Inge M W Verberk PhD , Marta del Campo PhD , Constance Delaby PhD , Joseph Therriault PhD , Joyce R Chong PhD , Sebastian Palmqvist PhD , Daniel Alcolea PhD
Blood biomarkers have emerged as accessible, cost-effective, and highly promising tools for advancing the diagnostics of Alzheimer’s disease. However, transitioning from cerebrospinal fluid biomarkers to blood biomarkers—eg, to verify amyloid β pathology—requires careful consideration. This Series paper highlights the main challenges in the implementation of blood biomarkers for Alzheimer’s disease in different possible contexts of use. Despite the robustness of measuring blood biomarker concentrations, the widespread adoption of blood biomarkers requires rigorous standardisation efforts to address inherent challenges in diverse contexts of use. The challenges include understanding the effect of pre-analytical and analytical conditions, potential confounding factors, and comorbidities that could influence outcomes of blood biomarkers and their use in diverse populations. Additionally, distinct scenarios present their own specific challenges. In memory clinics, the successful integration of blood biomarkers in diagnostic tests will require well-established diagnostic accuracy and comprehensive assessments of the effect of blood biomarkers on the diagnostic confidence and patient management of clinicians. In primary care settings, and even more when implemented in population-based screening programmes for which no experience with any biomarkers for Alzheimer’s disease currently exists, the implementation of blood biomarkers will be challenged by the need for education of primary care clinical staff and clear guidelines. However, despite the challenges, blood biomarkers hold great promise for substantially enhancing the diagnostic accuracy and effectively streamlining referral processes, leading to earlier diagnosis and access to treatments. The ongoing efforts that are shaping the integration of blood biomarkers across diverse clinical settings pave the way towards precision medicine in Alzheimer’s disease.
{"title":"Challenges in the practical implementation of blood biomarkers for Alzheimer’s disease","authors":"Prof Michael Schöll PhD , Inge M W Verberk PhD , Marta del Campo PhD , Constance Delaby PhD , Joseph Therriault PhD , Joyce R Chong PhD , Sebastian Palmqvist PhD , Daniel Alcolea PhD","doi":"10.1016/j.lanhl.2024.07.013","DOIUrl":"10.1016/j.lanhl.2024.07.013","url":null,"abstract":"<div><div>Blood biomarkers have emerged as accessible, cost-effective, and highly promising tools for advancing the diagnostics of Alzheimer’s disease. However, transitioning from cerebrospinal fluid biomarkers to blood biomarkers—eg, to verify amyloid β pathology—requires careful consideration. This Series paper highlights the main challenges in the implementation of blood biomarkers for Alzheimer’s disease in different possible contexts of use. Despite the robustness of measuring blood biomarker concentrations, the widespread adoption of blood biomarkers requires rigorous standardisation efforts to address inherent challenges in diverse contexts of use. The challenges include understanding the effect of pre-analytical and analytical conditions, potential confounding factors, and comorbidities that could influence outcomes of blood biomarkers and their use in diverse populations. Additionally, distinct scenarios present their own specific challenges. In memory clinics, the successful integration of blood biomarkers in diagnostic tests will require well-established diagnostic accuracy and comprehensive assessments of the effect of blood biomarkers on the diagnostic confidence and patient management of clinicians. In primary care settings, and even more when implemented in population-based screening programmes for which no experience with any biomarkers for Alzheimer’s disease currently exists, the implementation of blood biomarkers will be challenged by the need for education of primary care clinical staff and clear guidelines. However, despite the challenges, blood biomarkers hold great promise for substantially enhancing the diagnostic accuracy and effectively streamlining referral processes, leading to earlier diagnosis and access to treatments. The ongoing efforts that are shaping the integration of blood biomarkers across diverse clinical settings pave the way towards precision medicine in Alzheimer’s disease.</div></div>","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 10","pages":"Article 100630"},"PeriodicalIF":13.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/S2666-7568(24)00089-8
Simon Nusinovici PhD , Tyler Hyungtaek Rim MD , Hengtong Li MS , Marco Yu PhD , Mihir Deshmukh MS , Ten Cheer Quek BEng , Geunyoung Lee MS , Crystal Chun Yuen Chong MS , Qingsheng Peng MD , Can Can Xue PhD , Zhuoting Zhu MD , Emily Y Chew MD , Charumathi Sabanayagam PhD , Prof Tien-Yin Wong PhD , Yih-Chung Tham PhD , Prof Ching-Yu Cheng MD
<div><h3>Background</h3><div>Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age.</div></div><div><h3>Methods</h3><div>We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions, inflammation, and energy metabolism, and chronological age. We determined the performance of this biological ageing marker for the prediction of morbidity (cardiovascular disease and cancer events) and mortality (all-cause, cardiovascular disease, and cancer) in three independent cohorts (UK Biobank, the Singapore Epidemiology of Eye Diseases [SEED], and the Age-Related Eye Disease Study [AREDS] from the USA). We also compared the performance of RetiPhenoAge with two other known ageing biomarkers (hand grip strength and adjusted leukocyte telomere length) and one lifestyle factor (physical activity) for risk stratification of mortality and morbidity. We explored the underlying biology of RetiPhenoAge by assessing its associations with different systemic characteristics (eg, diabetes or hypertension) and blood metabolite levels. We also did a genome-wide association study to identify genetic variants associated with RetiPhenoAge, followed by expression quantitative trait loci mapping, a gene-based analysis, and a gene-set analysis. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and corresponding 95% CIs for the associations between RetiPhenoAge and the different morbidity and mortality outcomes.</div></div><div><h3>Findings</h3><div>Retinal photographs for 34 061 UK Biobank participants were used to train the model, and data for 9429 participants from the SEED cohort and for 3986 participants from the AREDS cohort were included in the study. RetiPhenoAge was associated with all-cause mortality (HR 1·92 [95% CI 1·42–2·61]), cardiovascular disease mortality (1·97 [1·02–3·82]), cancer mortality (2·07 [1·29–3·33]), and cardiovascular disease events (1·70 [1·17–2·47]), independent of PhenoAge and other possible confounders. Similar findings were found in the two independent cohorts (HR 1·67 [1·21–2·31] for cardiovascular disease mortality in SEED and 2·07 [1·10–3·92] in AREDS). RetiPhenoAge had stronger associations with mortality and morbidity than did hand grip strength, telomere length, and physical activity. We identified two genetic variants that were significantly associated with RetiPhenoAge (single
{"title":"Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study","authors":"Simon Nusinovici PhD , Tyler Hyungtaek Rim MD , Hengtong Li MS , Marco Yu PhD , Mihir Deshmukh MS , Ten Cheer Quek BEng , Geunyoung Lee MS , Crystal Chun Yuen Chong MS , Qingsheng Peng MD , Can Can Xue PhD , Zhuoting Zhu MD , Emily Y Chew MD , Charumathi Sabanayagam PhD , Prof Tien-Yin Wong PhD , Yih-Chung Tham PhD , Prof Ching-Yu Cheng MD","doi":"10.1016/S2666-7568(24)00089-8","DOIUrl":"10.1016/S2666-7568(24)00089-8","url":null,"abstract":"<div><h3>Background</h3><div>Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age.</div></div><div><h3>Methods</h3><div>We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions, inflammation, and energy metabolism, and chronological age. We determined the performance of this biological ageing marker for the prediction of morbidity (cardiovascular disease and cancer events) and mortality (all-cause, cardiovascular disease, and cancer) in three independent cohorts (UK Biobank, the Singapore Epidemiology of Eye Diseases [SEED], and the Age-Related Eye Disease Study [AREDS] from the USA). We also compared the performance of RetiPhenoAge with two other known ageing biomarkers (hand grip strength and adjusted leukocyte telomere length) and one lifestyle factor (physical activity) for risk stratification of mortality and morbidity. We explored the underlying biology of RetiPhenoAge by assessing its associations with different systemic characteristics (eg, diabetes or hypertension) and blood metabolite levels. We also did a genome-wide association study to identify genetic variants associated with RetiPhenoAge, followed by expression quantitative trait loci mapping, a gene-based analysis, and a gene-set analysis. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and corresponding 95% CIs for the associations between RetiPhenoAge and the different morbidity and mortality outcomes.</div></div><div><h3>Findings</h3><div>Retinal photographs for 34 061 UK Biobank participants were used to train the model, and data for 9429 participants from the SEED cohort and for 3986 participants from the AREDS cohort were included in the study. RetiPhenoAge was associated with all-cause mortality (HR 1·92 [95% CI 1·42–2·61]), cardiovascular disease mortality (1·97 [1·02–3·82]), cancer mortality (2·07 [1·29–3·33]), and cardiovascular disease events (1·70 [1·17–2·47]), independent of PhenoAge and other possible confounders. Similar findings were found in the two independent cohorts (HR 1·67 [1·21–2·31] for cardiovascular disease mortality in SEED and 2·07 [1·10–3·92] in AREDS). RetiPhenoAge had stronger associations with mortality and morbidity than did hand grip strength, telomere length, and physical activity. We identified two genetic variants that were significantly associated with RetiPhenoAge (single","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 10","pages":"Article 100593"},"PeriodicalIF":13.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.lanhl.2024.07.012
Jiaqi Gao PhD , Carlos F Mendes de Leon PhD , Boya Zhang PhD , Jennifer Weuve ScD , Kenneth M Langa PhD , Jennifer D'Souza PhD , Adam Szpiro PhD , Jessica Faul PhD , Joel D Kaufman MD , Richard Hirth PhD , Sara D Adar ScD
Background
Disability is a key marker of overall physical health in older adults and is often preceded by chronic disease. Although air pollution is a well recognised risk factor for multiple chronic diseases, its association with physical disability has not been well characterised. We investigated the associations of air pollutants with physical disability in a large cohort representative of older adults in the USA.
Methods
We used biennial data on incident activities of daily living (ADL) disability collected from respondents of the Health and Retirement Survey between 2000 and 2016. As part of the Environmental Predictors of Cognitive Health and Aging study, we estimated 10-year average PM2·5, PM10–2·5, nitrogen dioxide (NO2), and ozone (O3) concentrations at participant residences before each survey using spatiotemporal prediction models. We used a time-varying, weighted Cox model to estimate hazard ratios (HRs) for incident physical disability per interquartile increase of air pollution with detailed adjustments for confounders.
Findings
Among 15 411 respondents aged 65 years and older (mean age 70·2 [SD 6·5] years; 55% female, 45% male), 48% of respondents reported newly having ADL disability during a mean follow-up of 7·9 years (SD 4·7). In fully adjusted models, we found greater risks of ADL disability associated with higher concentrations of PM2·5 (HR 1·03 per 3·7 μg/m³ [95% CI 0·99–1·08], p=0·16), PM10–2·5 (1·05 per 4·9 μg/m³ [1·00–1·11], p=0·022), and NO2 (1·03 per 7·5 ppb [0·99–1·08]. p=0·064), although not all these associations were statistically significant. In contrast, O3 was associated with a lower risk of ADL disability (0·95 per 3·7 ppb [0·91–1·00], p=0·030). In a multi-pollutant model, associations were similar to the single-pollutant models for PM10–2·5 (1·05 per 4·9 μg/m³ [1·00–1·11], p=0·041) and O3 (0·94 per 3·7 ppb [0·88–1·01], p=0·083).
Interpretation
Our findings suggest that air pollution might be an underappreciated risk factor for physical disability in later life, although additional research is needed.
Funding
National Institutes of Environmental Health Sciences and National Institute on Aging.
背景:残疾是老年人整体身体健康的一个重要标志,而且往往先于慢性疾病。虽然空气污染是公认的多种慢性疾病的风险因素,但它与身体残疾的关系还没有得到很好的描述。我们在美国一个具有代表性的老年人大型队列中调查了空气污染物与身体残疾的关系:我们使用了 2000 年至 2016 年期间从健康与退休调查(Health and Retirement Survey)受访者处收集的两年一次的日常生活活动(ADL)残疾事件数据。作为认知健康和老龄化环境预测研究的一部分,我们使用时空预测模型估算了每次调查前受访者住所的 PM2-5、PM10-2-5、二氧化氮(NO2)和臭氧(O3)的 10 年平均浓度。我们使用时变加权 Cox 模型估算了空气污染每增加四分位数所导致的身体残疾的危险比(HRs),并对混杂因素进行了详细调整:在 15 411 名 65 岁及以上的受访者中(平均年龄 70-2 [SD 6-5] 岁;55% 为女性,45% 为男性),48% 的受访者表示在平均 7-9 年(SD 4-7 年)的随访期间新近出现了 ADL 残疾。在完全调整模型中,我们发现,PM2-5(HR 1-03 per 3-7 μg/m³ [95% CI 0-99-1-08],p=0-16)、PM10-2-5(1-05 per 4-9 μg/m³ [1-00-1-11],p=0-022)和 NO2(1-03 per 7-5 ppb [0-99-1-08],p=0-064)浓度越高,ADL 残疾的风险越大,但并非所有这些关联都具有统计学意义。相比之下,O3 与较低的 ADL 残疾风险相关(每 3-7 ppb 0-95 [0-91-1-00],p=0-030)。在多污染物模型中,PM10-2-5(1-05 per 4-9 μg/m³ [1-00-1-11],p=0-041)和臭氧(0-94 per 3-7 ppb [0-88-1-01],p=0-083)的相关性与单污染物模型相似:我们的研究结果表明,空气污染可能是导致晚年身体残疾的一个未被充分重视的风险因素,尽管还需要更多的研究:国家环境健康科学研究所和国家老龄化研究所。
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Research on neurodegenerative diseases has predominantly focused on high-income countries in the Global North. This Series paper describes the state of biomarker evidence for neurodegeneration in the Global South, including Latin America, Africa, and countries in south, east, and southeast Asia. Latin America shows growth in fluid biomarker and neuroimaging research, with notable advancements in genetics. Research in Africa focuses on genetics and cognition but there is a paucity of data on fluid and neuroimaging biomarkers. South and east Asia, particularly India and China, has achieved substantial progress in plasma, neuroimaging, and genetic studies. However, all three regions face several challenges in the form of a lack of harmonisation, insufficient funding, and few comparative studies both within the Global South, and between the Global North and Global South. Other barriers include scarce infrastructure, lack of knowledge centralisation, genetic and cultural diversity, sociocultural stigmas, and restricted access to tools such as PET scans. However, the diverse ethnic, genetic, economic, and cultural backgrounds in the Global South present unique opportunities for bidirectional learning, underscoring the need for global collaboration to enhance the understanding of dementia and brain health.
有关神经退行性疾病的研究主要集中在全球北方的高收入国家。本系列论文介绍了全球南部(包括拉丁美洲、非洲以及南亚、东亚和东南亚国家)神经变性生物标志物证据的现状。拉丁美洲在流体生物标志物和神经影像学研究方面取得了增长,在遗传学方面也取得了显著进展。非洲的研究重点是遗传学和认知,但有关体液和神经影像生物标志物的数据很少。南亚和东亚,尤其是印度和中国,在血浆、神经影像学和遗传学研究方面取得了重大进展。然而,这三个地区都面临着一些挑战,如缺乏统一性、资金不足、全球南部内部以及全球北部和全球南部之间的比较研究很少。其他障碍包括基础设施匮乏、知识缺乏集中性、遗传和文化多样性、社会文化污名以及 PET 扫描等工具的使用受到限制。然而,全球南方国家不同的种族、遗传、经济和文化背景为双向学习提供了独特的机会,突出了全球合作的必要性,以增进对痴呆症和大脑健康的了解。
{"title":"Biomarkers of neurodegeneration across the Global South","authors":"Eimear McGlinchey PhD , Claudia Duran-Aniotz PhD , Prof Rufus Akinyemi MD , Faheem Arshad MD , Eduardo R Zimmer PhD , Hanna Cho MD , Boluwatife Adeleye Adewale MD , Agustin Ibanez PhD","doi":"10.1016/S2666-7568(24)00132-6","DOIUrl":"10.1016/S2666-7568(24)00132-6","url":null,"abstract":"<div><div>Research on neurodegenerative diseases has predominantly focused on high-income countries in the Global North. This Series paper describes the state of biomarker evidence for neurodegeneration in the Global South, including Latin America, Africa, and countries in south, east, and southeast Asia. Latin America shows growth in fluid biomarker and neuroimaging research, with notable advancements in genetics. Research in Africa focuses on genetics and cognition but there is a paucity of data on fluid and neuroimaging biomarkers. South and east Asia, particularly India and China, has achieved substantial progress in plasma, neuroimaging, and genetic studies. However, all three regions face several challenges in the form of a lack of harmonisation, insufficient funding, and few comparative studies both within the Global South, and between the Global North and Global South. Other barriers include scarce infrastructure, lack of knowledge centralisation, genetic and cultural diversity, sociocultural stigmas, and restricted access to tools such as PET scans. However, the diverse ethnic, genetic, economic, and cultural backgrounds in the Global South present unique opportunities for bidirectional learning, underscoring the need for global collaboration to enhance the understanding of dementia and brain health.</div></div>","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 10","pages":"Article 100616"},"PeriodicalIF":13.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.1016/j.lanhl.2024.07.004
Prof Fei-Yuan Hsiao PhD , Elise Chia-Hui Tan PhD , Lin-Chieh Meng MS , Yi-Chin Lin PharmD MS , Ho-Min Chen MS , Shang-Ting Guan PharmD , Prof Der-Cherng Tarng MD PhD , Prof Chih-Yuan Wang MD PhD , Prof Liang-Kung Chen
<div><h3>Background</h3><p>GLP-1 receptor agonists and SGLT2 inhibitors are increasingly being used in people with type 2 diabetes on the basis of findings from randomised clinical trials; however, little is known of whether clinical outcomes are affected by frailty in real-world settings. We aimed to compare the clinical effectiveness and safety of GLP-1 receptor agonists and SGLT2 inhibitors in managing type 2 diabetes, with a specific focus on stratifying people by their frailty status.</p></div><div><h3>Methods</h3><p>In this retrospective, nationwide, longitudinal study, we identified people (aged ≥20 years) with type 2 diabetes who newly initiated either a GLP-1 receptor agonist or an SGLT2 inhibitor during the period Jan 1, 2017 to Dec 31, 2019 from the Taiwan National Health Insurance database. Individuals were excluded if they had been diagnosed with cancer, received dialysis for kidney failure, or had prescriptions for a GLP-1 receptor agonist or an SGLT2 inhibitor, within 1 year before the index date. Mortality data were collected from the Taiwan National Death Registry. Eligible individuals were categorised into three frailty subgroups—fit, mild frailty, and moderate or severe frailty—on the basis of the multimorbidity frailty index. Propensity score matching (1:1) was used to balance covariates between recipients of GLP-1 receptor agonists and SGLT2 inhibitors among each frailty subgroup. Clinical outcomes of interest included three-point major adverse cardiovascular events (non-fatal acute myocardial infarction, non-fatal stroke, and fatal cardiovascular disease), all-cause mortality, hospitalisation for heart failure, dialysis or renal transplant, severe diabetic foot complications, retinopathy, hospitalisation for severe hyperglycaemia, and hospitalisation for severe hypoglycaemia. The association between the use of a GLP-1 receptor agonist versus an SGLT2 inhibitor and the risk of the outcomes of interest among each frailty subgroup was examined using a subdistribution hazard model.</p></div><div><h3>Findings</h3><p>We identified 320 210 people with type 2 diabetes, of whom 280 163 met the eligibility criteria, who initiated either a GLP-1 receptor agonist (n=22 968; mean age 57·7 years [SD 13·9], 11 338 [49·4%] were female, and 11 630 [50·6%] were male) or SGLT2 inhibitor (n=257 195; mean age 58·8 years [12·3], 107 988 [42·0%] were female, and 149 207 [58·0%] were male) during 2017–19. After matching, 11 882, 7210, and 3414 pairs of GLP-1 receptor agonist and SGLT2 inhibitor users were assigned in the fit, mild frailty, and moderate or severe frailty subgroups. All clinical outcomes were comparable between users of GLP-1 receptor agonists and SGLT2 inhibitors among each frailty subgroup, except for a higher risk of hospitalisation for severe hyperglycaemia with GLP-1 receptor agonists than with SGLT2 inhibitors in the mild frailty subgroup (subdistribution hazard ratio 1·25 [95% CI 1·13–1·38]; p<0·0001) and a higher risk of d
{"title":"Effect of frailty on effectiveness and safety of GLP-1 receptor agonists versus SGLT2 inhibitors in people with type 2 diabetes in Taiwan: a retrospective, nationwide, longitudinal study","authors":"Prof Fei-Yuan Hsiao PhD , Elise Chia-Hui Tan PhD , Lin-Chieh Meng MS , Yi-Chin Lin PharmD MS , Ho-Min Chen MS , Shang-Ting Guan PharmD , Prof Der-Cherng Tarng MD PhD , Prof Chih-Yuan Wang MD PhD , Prof Liang-Kung Chen","doi":"10.1016/j.lanhl.2024.07.004","DOIUrl":"10.1016/j.lanhl.2024.07.004","url":null,"abstract":"<div><h3>Background</h3><p>GLP-1 receptor agonists and SGLT2 inhibitors are increasingly being used in people with type 2 diabetes on the basis of findings from randomised clinical trials; however, little is known of whether clinical outcomes are affected by frailty in real-world settings. We aimed to compare the clinical effectiveness and safety of GLP-1 receptor agonists and SGLT2 inhibitors in managing type 2 diabetes, with a specific focus on stratifying people by their frailty status.</p></div><div><h3>Methods</h3><p>In this retrospective, nationwide, longitudinal study, we identified people (aged ≥20 years) with type 2 diabetes who newly initiated either a GLP-1 receptor agonist or an SGLT2 inhibitor during the period Jan 1, 2017 to Dec 31, 2019 from the Taiwan National Health Insurance database. Individuals were excluded if they had been diagnosed with cancer, received dialysis for kidney failure, or had prescriptions for a GLP-1 receptor agonist or an SGLT2 inhibitor, within 1 year before the index date. Mortality data were collected from the Taiwan National Death Registry. Eligible individuals were categorised into three frailty subgroups—fit, mild frailty, and moderate or severe frailty—on the basis of the multimorbidity frailty index. Propensity score matching (1:1) was used to balance covariates between recipients of GLP-1 receptor agonists and SGLT2 inhibitors among each frailty subgroup. Clinical outcomes of interest included three-point major adverse cardiovascular events (non-fatal acute myocardial infarction, non-fatal stroke, and fatal cardiovascular disease), all-cause mortality, hospitalisation for heart failure, dialysis or renal transplant, severe diabetic foot complications, retinopathy, hospitalisation for severe hyperglycaemia, and hospitalisation for severe hypoglycaemia. The association between the use of a GLP-1 receptor agonist versus an SGLT2 inhibitor and the risk of the outcomes of interest among each frailty subgroup was examined using a subdistribution hazard model.</p></div><div><h3>Findings</h3><p>We identified 320 210 people with type 2 diabetes, of whom 280 163 met the eligibility criteria, who initiated either a GLP-1 receptor agonist (n=22 968; mean age 57·7 years [SD 13·9], 11 338 [49·4%] were female, and 11 630 [50·6%] were male) or SGLT2 inhibitor (n=257 195; mean age 58·8 years [12·3], 107 988 [42·0%] were female, and 149 207 [58·0%] were male) during 2017–19. After matching, 11 882, 7210, and 3414 pairs of GLP-1 receptor agonist and SGLT2 inhibitor users were assigned in the fit, mild frailty, and moderate or severe frailty subgroups. All clinical outcomes were comparable between users of GLP-1 receptor agonists and SGLT2 inhibitors among each frailty subgroup, except for a higher risk of hospitalisation for severe hyperglycaemia with GLP-1 receptor agonists than with SGLT2 inhibitors in the mild frailty subgroup (subdistribution hazard ratio 1·25 [95% CI 1·13–1·38]; p<0·0001) and a higher risk of d","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 9","pages":"Article 100621"},"PeriodicalIF":13.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666756824001375/pdfft?md5=e893d77bc3881c4e4d26dc5179146936&pid=1-s2.0-S2666756824001375-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.1016/j.lanhl.2024.07.010
Roemer J Janse , Yvette Meuleman
{"title":"Improving health-related quality of life after kidney transplantation using lifestyle interventions","authors":"Roemer J Janse , Yvette Meuleman","doi":"10.1016/j.lanhl.2024.07.010","DOIUrl":"10.1016/j.lanhl.2024.07.010","url":null,"abstract":"","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 9","pages":"Article 100627"},"PeriodicalIF":13.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666756824001430/pdfft?md5=b9ef50fa11daa29e39a67a7641e67bff&pid=1-s2.0-S2666756824001430-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><h3>Background</h3><p>Health-related quality of life (HRQoL) is highly valued among older adults with cancer. The Geriatric 8 screening tool identifies individuals with frailty, but its association with HRQoL remains sparsely investigated. Herein, we evaluate whether Geriatric 8 frailty is associated with short-term and long-term HRQoL in older patients with cancer.</p></div><div><h3>Methods</h3><p>In this Danish single-centre, prospective cohort study, patients aged 70 years and older, referred to oncological assessment for solid cancers, were screened with the Geriatric 8. Patients completed the European Organisation for Research and Treatment of Cancer (EORTC) Quality-of-Life Core 30 (QLQ-C30) and Elderly 14 (ELD14) questionnaires at baseline, 3 months, 6 months, 9 months, and 12 months. Patient characteristics were obtained from medical records. Differences in mean global health status and QoL (GHS), measured using the two seven-point Likert scale questions from the EORTC QLQ-C30 regarding overall health and QoL during the past week, between patients with frailty (defined as a Geriatric 8 score of ≤14) and without frailty within 12 months were the primary outcome. Secondary outcomes were differences in the mean EORTC Summary Score comprised of all questions from the QLQ-C30 except for those included in the GHS and a question concerning financial difficulties, and five functional (physical, role, and social functioning, maintaining purpose, and family support from the EORTC QLQ-C30 and the EORTC-QLQ-ELD14), and five symptom scales (fatigue, pain, mobility, future worries, and burden of illness from the EORTC-QLQ-C30 and the EORTC-QLQ-ELD14). Analyses were done using linear mixed models. All primary and secondary outcomes were adjusted for gender, treatment intent, and cancer type and the primary outcome was also assessed by means of a responder analysis.</p></div><div><h3>Findings</h3><p>Between June 1, 2020 and Oct 15, 2021, 1398 eligible patients were screened with the Geriatric 8 (908 [65%] with frailty and 490 [35%] without frailty) and provided medical record data. Of these patients, 707 (51%) also provided HRQoL data (437 [62%] with frailty and 270 [38%] without frailty). When adjusted, patients with frailty had poorer GHS (–15·1, 95% CI –18·5 to –11·6; p<0·0001) at baseline and throughout follow-up (3 months –7·4, –11·0 to –3·7, p=0·0001; 6 months –11·7, –15·5 to –7·9, p<0·0001; 9 months –10·4, –14·3 to –6·5, p<0·0001; 12 months –10·4, –14·6 to –6·2, p<0·0001) compared to patients without frailty. Adjusted summary scores were also poorer for patients with frailty (–9·9, 95% CI –12·1 to –7·6; p<0·0001) compared to patients without frailty at baseline and throughout follow-up (3 months –8·2, –10·5 to –5·8, p=0·0001; 6 months –9·0, –11·4 to –6·6, p<0·0001; 9 months –9·2, –11·7 to –6·8, p<0·0001; 12 months –8·9, –11·5 to –6·3, p<0·0001). Patients with frailty had significantly worse physical and role functioning
{"title":"Association between Geriatric 8 frailty and health-related quality of life in older patients with cancer (PROGNOSIS-G8): a Danish single-centre, prospective cohort study","authors":"Helena Møgelbjerg Ditzel MD , Ann-Kristine Weber Giger MD PhD , Cecilia Margareta Lund MD PhD , Prof Henrik Jørn Ditzel MD DMSc , Sören Möller PhD , Prof Per Pfeiffer MD PhD , Prof Jesper Ryg MD PhD , Prof Marianne Ewertz MD DMSc , Trine Lembrecht Jørgensen MD PhD","doi":"10.1016/S2666-7568(24)00118-1","DOIUrl":"10.1016/S2666-7568(24)00118-1","url":null,"abstract":"<div><h3>Background</h3><p>Health-related quality of life (HRQoL) is highly valued among older adults with cancer. The Geriatric 8 screening tool identifies individuals with frailty, but its association with HRQoL remains sparsely investigated. Herein, we evaluate whether Geriatric 8 frailty is associated with short-term and long-term HRQoL in older patients with cancer.</p></div><div><h3>Methods</h3><p>In this Danish single-centre, prospective cohort study, patients aged 70 years and older, referred to oncological assessment for solid cancers, were screened with the Geriatric 8. Patients completed the European Organisation for Research and Treatment of Cancer (EORTC) Quality-of-Life Core 30 (QLQ-C30) and Elderly 14 (ELD14) questionnaires at baseline, 3 months, 6 months, 9 months, and 12 months. Patient characteristics were obtained from medical records. Differences in mean global health status and QoL (GHS), measured using the two seven-point Likert scale questions from the EORTC QLQ-C30 regarding overall health and QoL during the past week, between patients with frailty (defined as a Geriatric 8 score of ≤14) and without frailty within 12 months were the primary outcome. Secondary outcomes were differences in the mean EORTC Summary Score comprised of all questions from the QLQ-C30 except for those included in the GHS and a question concerning financial difficulties, and five functional (physical, role, and social functioning, maintaining purpose, and family support from the EORTC QLQ-C30 and the EORTC-QLQ-ELD14), and five symptom scales (fatigue, pain, mobility, future worries, and burden of illness from the EORTC-QLQ-C30 and the EORTC-QLQ-ELD14). Analyses were done using linear mixed models. All primary and secondary outcomes were adjusted for gender, treatment intent, and cancer type and the primary outcome was also assessed by means of a responder analysis.</p></div><div><h3>Findings</h3><p>Between June 1, 2020 and Oct 15, 2021, 1398 eligible patients were screened with the Geriatric 8 (908 [65%] with frailty and 490 [35%] without frailty) and provided medical record data. Of these patients, 707 (51%) also provided HRQoL data (437 [62%] with frailty and 270 [38%] without frailty). When adjusted, patients with frailty had poorer GHS (–15·1, 95% CI –18·5 to –11·6; p<0·0001) at baseline and throughout follow-up (3 months –7·4, –11·0 to –3·7, p=0·0001; 6 months –11·7, –15·5 to –7·9, p<0·0001; 9 months –10·4, –14·3 to –6·5, p<0·0001; 12 months –10·4, –14·6 to –6·2, p<0·0001) compared to patients without frailty. Adjusted summary scores were also poorer for patients with frailty (–9·9, 95% CI –12·1 to –7·6; p<0·0001) compared to patients without frailty at baseline and throughout follow-up (3 months –8·2, –10·5 to –5·8, p=0·0001; 6 months –9·0, –11·4 to –6·6, p<0·0001; 9 months –9·2, –11·7 to –6·8, p<0·0001; 12 months –8·9, –11·5 to –6·3, p<0·0001). Patients with frailty had significantly worse physical and role functioning","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 9","pages":"Article 100612"},"PeriodicalIF":13.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666756824001181/pdfft?md5=535fb239e0e39156a71823a9ffaf776a&pid=1-s2.0-S2666756824001181-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142112841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.1016/j.lanhl.2024.07.009
Larissa Zwar
{"title":"Psychosocial pathways linking differences in socioeconomic status to symptoms of depression","authors":"Larissa Zwar","doi":"10.1016/j.lanhl.2024.07.009","DOIUrl":"10.1016/j.lanhl.2024.07.009","url":null,"abstract":"","PeriodicalId":34394,"journal":{"name":"Lancet Healthy Longevity","volume":"5 9","pages":"Article 100626"},"PeriodicalIF":13.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666756824001429/pdfft?md5=feca87aea0a11b8f7dc3c5c07e122d86&pid=1-s2.0-S2666756824001429-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142112842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}