Biological age in diabetes and precision medicine

Cortez, Briana; Bahour, Nadine; Aguayo-Mazzucato, Cristina

doi:10.18632/aging.204123

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Hence, considering biological age, in addition to chronological age, may also provide a tool to accurately assess healthspan in T2DM. 7 Older adults with T2DM constitute a diverse group of patients characterized by varying functional capabilities, living arrangements, concomitant medical conditions, and life expectancies. 8 Moreover, there is broad variation in functional status and health-related quality of life within every age subgroup (ie, from pre-old to oldest-old), with individuals having good and poor health at both ends of the age spectrum.…”

Section: Introductionmentioning

confidence: 99%

Management of Glucose-Lowering Therapy in Older Adults with Type 2 Diabetes: Challenges and Opportunities

Doucet,

Gourdy,

Meyer

et al. 2023

CIA

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The population of older adults (≥65 years) with type 2 diabetes mellitus (T2DM) is diverse, encompassing individuals with varying functional capabilities, living arrangements, concomitant medical conditions, and life expectancies. Hence, their categorization into different patient profiles (ie, good health, intermediate health, poor health) may aid in clinical decision-making when establishing glycemic goals and pharmacological treatment strategies. Further granularity in assessing each patient profile through interdisciplinary collaboration may also add precision to therapeutic and monitoring decisions. In this review, we discuss with a multidisciplinary approach how to deliver the best benefit from advanced diabetes therapies and technologies to older adults with T2DM according to each patient profile. There remain however several areas that deserve further research in older adults with T2DM, including the efficacy and safety of continuous glucose monitoring and automated insulin delivery systems, the switch to once-weekly insulin, the effectiveness of multidisciplinary care models, and the use of supported telemedicine and remote blood glucose monitoring in the oldest-old (≥85 years) who particularly require the assistance of others.

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Section: Introductionmentioning

confidence: 99%

Management of Glucose-Lowering Therapy in Older Adults with Type 2 Diabetes: Challenges and Opportunities

Doucet,

Gourdy,

Meyer

et al. 2023

CIA

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“…Studies have shown that the biological markers based age can be a reliable indicator of an individual's likelihood of developing certain health conditions. This includes chronic diseases such as cardiovascular disease 18 , type 2 diabetes 19 , and neurological disease 20 . One of the advantages of using phenotypic age as a predictive tool is that it can provide more accurate information than chronological age or sole marker (e.g., telomere) alone 21,22 .…”

mentioning

confidence: 99%

Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study

You,

Chen,

Liu

et al. 2024

Sci Rep

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Sleep is a modifiable behavior that can be targeted in interventions aimed at promoting healthy aging. This study aims to (i) identify the sleep duration trend in US adults; (ii) investigate the relationship between sleep duration and phenotypic age; and (iii) explore the role of exercise in this relationship. Phenotypic age as a novel index was calculated according to biomarkers collected from US adults based on the National Health and Nutrition Examination Survey (NHANES). Sleep information was self-reported by participants and discerned through individual interviews. The principal analytical method employed was weighted multivariable linear regression modeling, which accommodated for the complex multi-stage sampling design. The potential non-linear relationship was explored using a restricted cubic spline (RCS) model. Furthermore, subgroup analyses evaluated the potential effects of sociodemographic and lifestyle factors on the primary study outcomes. A total of 13,569 participants were finally included in, thereby resulting in a weighted population of 78,880,615. An examination of the temporal trends in sleep duration revealed a declining proportion of individuals with insufficient and markedly deficient sleep time since the 2015–2016 cycle. Taken normal sleep group as a reference, participants with extreme short sleep [β (95% CI) 0.582 (0.018, 1.146), p = 0.044] and long sleep [β (95% CI) 0.694 (0.186, 1.203), p = 0.010] were both positively associated with phenotypic age using the fully adjusted model. According to the dose–response relationship between sleep duration and phenotypic age, long sleep duration can benefit from regular exercise activity, whereas short sleep duration with more exercise tended to have higher phenotypic age. There is an inverted U-shaped relationship between short and long sleep durations and phenotypic age. This study represents an important step forward in our understanding of the complex relationship between sleep and healthy aging. By shedding light on this topic and providing practical exercise recommendations for promoting healthy sleep habits, researchers can help individuals live longer, healthier, and more fulfilling lives.

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Biological age in diabetes and precision medicine

Cited by 2 publications

References 5 publications

Management of Glucose-Lowering Therapy in Older Adults with Type 2 Diabetes: Challenges and Opportunities

Management of Glucose-Lowering Therapy in Older Adults with Type 2 Diabetes: Challenges and Opportunities

Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study

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