&lt;p&gt;Current and emerging biomarkers of frailty in the elderly&lt;/p&gt;

Saedi, Ahmed Al; Feehan, Jack; Phu, Steven; Duque, Gustavo

doi:10.2147/cia.s168687

Cited by 130 publications

(112 citation statements)

References 119 publications

(115 reference statements)

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“…Firstly, we need to differentiate between biomarkers of ageing and those that reflect frailty. Much work is being done in this area (reviewed in [48]). Secondly, the use of high-throughput tools such as transcriptomics, proteomics, metabolomics whilst very important in advancing the biological understanding, in themselves are not suitable biomarkers for everyday use.…”

Section: Biomarkers Of Frailtymentioning

confidence: 99%

Defining the vulnerable patient with myeloma—a frailty position paper of the European Myeloma Network

et al. 2020

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As the treatment landscape continues to evolve towards the application of precision medicine in multiple myeloma (MM), there is a clear need to identify those patients who are at risk of not achieving the maximum benefit whilst exposed to the highest level of toxicity. This group of patients, defined as frail, is an unmet clinical need. However, how we define such a vulnerable group of patients with MM remains to be clarified. An integral aspect of this is to define the physiological age and capacity of patients with MM to deal with the burden of their disease and it's treatment. Such assessments may include not only functional and clinical assessments but also laboratory-based biomarkers of frailty, aging and senescent cellular burden. A need to develop, test and validate clinical screening scores before their adoption into clinical practice is mandated. This position paper from the European Myeloma Network aims to review what is known about defining frailty in MM, and how we can advance this knowledge for the design of clinical trials and ultimately how we deliver treatment in the clinic.

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Section: Biomarkers Of Frailtymentioning

confidence: 99%

Defining the vulnerable patient with myeloma—a frailty position paper of the European Myeloma Network

et al. 2020

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“…3 Although growing attention has been paid to frailty, the lack of valid biomarkers and effective intervention targets for frailty remains a problematic issue. 4 Emerging evidence implies a crucial role of the gut microbiota in frailty among older adults. 5,6 The gut microbiota may modulate aging-related alterations in physical performance, sarcopenia, and cognitive impairment, all of which are elements of frailty.…”

Section: Introductionmentioning

confidence: 99%

<p>Trimethylamine N-Oxide, a Gut Microbiota-Dependent Metabolite, is Associated with Frailty in Older Adults with Cardiovascular Disease</p>

He¹,

Luo²,

Liu³

et al. 2020

CIA

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Objective: Our study aimed to explore the association between trimethylamine N-oxide and frailty in older adults with cardiovascular disease. Patients and Methods: This cross-sectional study analyzed a total of 451 people aged 65 years or older who underwent comprehensive geriatric assessments. Frailty status was determined using a frailty index constructed with 48 variables according to the cumulative deficits model. Physical frailty and cognitive frailty were also assessed in detail. Fasting plasma TMAO was measured by mass spectrometry. Results: The proportion of frail subjects was 29.9% (135/451). Plasma TMAO levels were significantly higher in frail patients than in nonfrail individuals (4.04 [2.84-7.01] vs 3.21 [2.13-5.03] µM; p<0.001). Elevated plasma TMAO levels were independently associated with the likelihood of frailty (OR 2.12, 95% CI 1.01-4.38, p=0.046). Dose-response analysis revealed a linear association between the TMAO concentration and the OR for frailty. A 2-unit increase in TMAO was independently correlated with physical frailty (OR 1.23, 95% CI 1.08-1.41, p for trend 0.002) and cognitive frailty (OR 1.21, 95% CI 1.01-1.45, p for trend 0.04). Conclusion: Elevated circulating TMAO levels are independently associated with frailty among older adults with cardiovascular disease.

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“…Although glucose control is only one aspect of the holistic treatment of diabetes, and HbA1c may, in some patients, be a marker of conditions other than glucose control (ie, frailty), to our knowledge no models have yet been developed to predict the durability of glycaemic control in patients with T2D following first‐line metformin monotherapy failure. A major strength of this work is the availability of data from multiple countries, which enhances the generalizability of the findings, a key attribute for prognostic model development.…”

Section: Discussionmentioning

confidence: 99%

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

Ling

Park

Zaccardi

et al. 2020

Diabetes Obesity Metabolism

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Aim: To develop and internally validate prognostic models on the long-term durability of glycaemic control in patients with type 2 diabetes after metformin failure.Materials and Methods: DISCOVER is a 3-year, prospective observational study across six continents investigating second-line glucose-lowering therapies. In this analysis from 35 countries, we included patients on metformin initiating secondline glucose-lowering medication(s) because of physician-defined lack of efficacy.The outcome was durability of glycaemic control, defined as three consecutive levels of HbA1c at 6-, 12-and 24-month follow-up at target (HbA1c equal to or lower than the level when the physician initiated the second-line therapy in patients with baseline HbA1c ≤7% [53 mmol/mol]; and equal to or lower than 7% in those with baseline HbA1c >7%). We developed and internally validated two prognostic models: a base model, which included age, sex, ethnicity, country income group, baseline HbA1c and second-line therapy, and an advanced model, established through statistical variable selections from a model including base variables and 13 additional predictors selected from a literature review. We used logistic regression to develop and 500 bootstrapping samples to internally validate the models; discrimination and calibration were used to assess model performance.Results: Overall, 896 out of 2995 participants (29.9%) had sustained glycaemic control. The base model performed well: Nagelkerke R 2 was 0.13, C-index 0.70 (95% CI: 0.68, 0.71) and bias-corrected C-index 0.69 after internal validation. Diabetes duration, insurance type, estimated glomerular filtration rate and glucose self-monitoring were additionally selected in the advanced model, which had only a slightly better performance compared with the base model: Nagelkerke R 2 0.20, C-index 0.71 (95% CI: 0.69, 0.73) and bias-corrected C-index 0.70. Calibration plots showed good calibrations of both validated models.

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<p>Current and emerging biomarkers of frailty in the elderly</p>

Cited by 130 publications

References 119 publications

Defining the vulnerable patient with myeloma—a frailty position paper of the European Myeloma Network

Defining the vulnerable patient with myeloma—a frailty position paper of the European Myeloma Network

<p>Trimethylamine N-Oxide, a Gut Microbiota-Dependent Metabolite, is Associated with Frailty in Older Adults with Cardiovascular Disease</p>

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

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