Frailty detection with routine blood tests: based on the English longitudinal study of ageing (ELSA)

doi:10.21203/rs.3.rs-4353538/v1

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Research Article

Frailty detection with routine blood tests: based on the English longitudinal study of ageing (ELSA)

https://doi.org/10.21203/rs.3.rs-4353538/v1

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Purpose

Frailty is a rising global health issue in ageing society. Easily accessible and sensitive tools are needed for frailty monitoring while routine blood factors can be potential candidates.

Methods

Data of 1907 participants (aged 60 years or above) were collected from the 4th to 9th wave of the English longitudinal study of ageing. 14 blood factors obtained from blood tests were included in the analysis. A 52-item frailty index (FI) was calculated for frailty evaluation. Logistic regression and Cox proportional hazards analysis were used to explore the relationships between baseline blood factors and the occurrence of frailty over time respectively. All analyses were controlled for age and sex.

Results

Our study identified that 8 blood factors (mean corpuscular haemoglobin, HDL, triglyceride, ferritin, hsCRP, dehydroepiandrosterone, haemoglobin, and WBC) involved in inflammatory, nutritional and metabolic processes were associated with frailty. The combined model with these 8 blood factors had an AUC of 0.758 at cross-sectional level. In the Cox proportional hazards analysis, higher triglyceride (HR: 1.30, 95%CI: 1.07 ~ 1.59), WBC (HR: 1.16, 95%CI: 1.05 ~ 1.28), and lower HDL (HR: 0.58, 95%CI: 0.38 ~ 0.90) at baseline were linked to greater risk of developing frailty within 10 years. Compared to adults without abnormal blood factors at baseline, the hazard ratios of participants with two or more abnormal blood factors were almost 2-fold higher in developing frailty over time.

Conclusions

Routine blood factors, particularly triglyceride, HDL and WBC, could be used for frailty screening in clinical practice and estimate the development of frailty over time.

ageing

frailty detection

blood factors

routine

Frailty, also known as frailty syndrome, is a clinical state characterized by declines in physical homeostasis reserve and anti-stress ability¹. It is estimated that the overall prevalence of frailty has reached 12% (evaluated by Fried frailty phenotype) and 24% (evaluated by frailty index [FI]) among middle-aged and older adults in the world². The occurrence of frailty increases the risk of adverse events such as falls, hospitalization, and even death³, putting a heavy burden on families and healthcare systems. Therefore, an early detection of frailty is critical as timely interventions can be provided to delay or even prevent the onset.

Nearly 70 frailty assessment tools have been developed in recent years with Fried frailty phenotype and the FI as the most popular ones⁴. Of note, most of the frailty screening tools only consist of subjective assessments that take considerable time to answer⁵, convenient and objective indices that can be used for frailty monitoring are still lacking. Since frailty is a multifaceted geriatric syndrome that is closely associated with multiple pathophysiological processes such as metabolic disorders, chronic low-grade inflammation and oxidative stress^6–8, relevant biological factors involved in these processes have received increasing research interests as potential markers for frailty. Existing evidence has shown that circulating levels of inflammatory markers (e.g., high-sensitivity C-reactive protein [hsCRP]⁹, interleukin 6 [IL-6]¹⁰ and white blood cells [WBC]¹¹), hormones (e.g., insulin-like growth factor 1 [IGF-1]¹², dehydroepiandrosterone [DHEA]¹³ and myostatin¹⁴), metabolic factors (e.g., glucose¹⁵ and triglyceride [TG]¹⁶) and nutritional factors (e.g., haemoglobin [Hgb]¹⁷ and 25 (OH) D¹⁸) are related to the occurrence of frailty in older people. However, most association studies were limited within a specific pathophysiological aspect. An exploration on the combined impact of diverse physiological processes on frailty is still needed. Additionally, an evaluation tool based on easily accessible blood markers involved in various physiological processes might be convenient and sensitive for frailty screening¹⁹.

This study aimed to explore the possibility of frailty screening and monitoring using blood factors obtained from routine blood tests. Based on the English longitudinal study of ageing (ELSA), cross-sectional associations were examined between 14 routine blood factors and frailty status. Survival analyses were further performed to analyze the associations between identified blood factors and the development of frailty over a 10-year period.

Study design and baseline population

The ELSA is a nationally representative study of adults aged 50 years and over living in private households in the United Kingdom. The first survey was conducted in 2002–2003, follow-up data were collected every 2 years through computer-assisted interviews and self-completed questionnaires, biological samples were collected and physical examinations were performed every 4 years through a nurse's home visit. The ELSA has received ethical approval from the multi-centre Research and Ethics Committee. Written consent forms were signed by all participants.

This study included data of older adults (aged 60 years or above) from wave 4 to wave 9 (over a 10-year period from 2008 to 2018) in the ELSA cohort. In wave 4, there were 7321 older adults aged 60 years or above. 1636 adults were excluded due to missing data in the FI items, 3595 adults with missing blood tests were also excluded, and 183 individuals were removed for acute inflammatory condition (hsCRP concentration over 10mg/L or WBC counts more than 10.0×10⁹/L). Finally, 1907 older adults were enrolled in this study. Figure 1 shows the selection process of the analytical sample.

Blood tests

Blood samples were collected by nurses every four years from all participants except for those with clotting or bleeding disorders, a history of fits or convulsions, or on anticoagulant drug (e.g. warfarin, protamine, acenocoumarol) treatment.

Blood tests were conducted at the Royal Victoria Infirmary (Newcastle-upon-Tyne, UK) and written consent was obtained from all participants. Details of the test procedure can be found in the Health Survey for England technical report²⁰. 14 blood markers were tested in the ELSA and these markers were all included for analysis in the current study.

Frailty evaluation

Frailty is defined by the FI, which is calculated as the ratio of the number of functional impairments to the total number of functional assessments. An FI has a range from 0 to 1. It has been suggested that an FI value of less than or equal to 0.12 indicates that an individual is robust while an FI value of greater than 0.25 indicates that an individual is frail²¹. An FI value calculated from over 30 functional assessments is assumed to be comparable across studies²².

In the current study, the FI at each wave was calculated based on 52 items that covered six domains, i.e., activities of daily living (ADL), mobility limitation, chronic diseases, self-rated health, psychological symptoms and cognitive functioning. Each item was equally assigned to one score and the value of each choice was determined by the rank (Supplementary Table 1). For example, the values of choices in hearing impairment were assigned to 0, 0.25, 0.5, 0.75 and 1 point, corresponding to the choices of “excellent”, “very good”, “good”, “fair” and “bad”, respectively. Additionally, the item “orientation in time” was calculated based on four variables from the Mini-Mental State Examination (MMSE), in which the deficit of each variable was given 0.25 point.

Statistical analysis

In this study, descriptive data were presented as mean (standard deviation), median value (interquartile range) or count (percentage), as appropriate. Independent t-test, median test and Chi-square test were used to compare the statistical difference of baseline data. Data of blood factors and frailty status at baseline (wave 4) were used to identify frailty-related blood factors. Multivariate logistic regression models were first performed to analyse the association of combined blood factors with frailty. Blood factors which were significant in multivariate logistic regression were regarded as frailty-related indicators in routine blood tests. In the multivariate logistic regression analysis, a variance inflated factor (VIF) value of 10 was used to exclude blood factors that demonstrated high collinearity with other indicators. To examine whether the concentrations of identified frailty-related factors at baseline (wave 4) could predict the development of frailty, cox proportional hazards (CoxPH) models were performed over a 10-year period (wave 4 to wave 9). Additionally, an exploration on the number of abnormal factors and frailty development was also performed using the CoxPH. Abnormality of these blood factors were defined by clinical reference values, published papers or quartile values^23–26 (Supplementary Table 2). All analyses were controlled for age and sex, and were performed by R 4.2.1²⁷. Two-sided test was used and P < 0.05 was considered as statistically significant. Considering that the concentrations of some blood factors (e.g., Hgb²⁸ and CRP²⁹) are sex-specific¹⁰, sensitivity analyses were further performed on male and female adults separately.

Baseline characteristics of participants

The mean age of participants was 67.3 years and 47.2% of them were male. The median FI value in the whole participant group was 0.07 and the female group had a higher FI value than the male group (P < 0.001). The prevalence of frailty in females (8.3%) was also higher than that in males (5.1%). Compared with the male, the female had lower TG, ferritin, DHEA, Hgb, fasting blood sugar, mean corpuscular haemoglobin (MCH), IGF-1 and less WBC. Lipoproteins, cholesterol, hsCRP and fibrinogen concentrations were higher in the female (Table 1).

Table 1

Baseline characteristics of participants
Variables	All participants (n = 1907)	Male (n = 855)	Female (n = 923)	P
Age (years), mean (SD)	67.3 (5.3)	67.3 (5.3)	67.2 (5.3)	0.49
Frailty Index, median (IQR)	0.07 (0.04 ~ 0.13)	0.06 (0.03 ~ 0.12)	0.09 (0.05 ~ 0.15)	< 0.001
Frailty, n (%)	129 (6.8%)	46 (5.1%)	83 (8.3%)	< 0.001
TG (mmol/L), median (IQR)	1.30 (1.00 ~ 1.80)	1.40 (1.00 ~ 1.90)	1.30 (1.00 ~ 1.80)	< 0.01
HDL (mmol/L), median (IQR)	1.60 (1.30 ~ 1.80)	1.40 (1.20 ~ 1.60)	1.70 (1.50 ~ 2.00)	< 0.001
LDL (mmol/L), median (IQR)	3.30 (2.70 ~ 4.10)	3.10 (2.50 ~ 3.80)	3.50 (2.80 ~ 4.20)	< 0.001
Rtin (ng/ml), median (IQR)	99.00 (57.00 ~ 166.00)	132.00 (74.00 ~ 212.00)	82.00 (49.00 ~ 125.25)	< 0.001
hsCRP (mg/L), median (IQR)	1.70 (0.90 ~ 3.20)	1.60 (0.80 ~ 3.00)	1.80 (0.90 ~ 3.30)	< 0.05
Cfib (g/L), median (IQR)	3.30 (3.00 ~ 3.60)	3.30 (3.00 ~ 3.60)	3.40 (3.10 ~ 3.70)	< 0.01
DHEA (umol/L), median (IQR)	1.90 (1.10 ~ 2.90)	2.40 (1.60 ~ 3.80)	1.40 (0.90 ~ 2.20)	< 0.001
Hgb (g/dL), median (IQR)	14.30 (13.50 ~ 15.10)	15.00 (14.30 ~ 15.70)	13.80 (13.10 ~ 14.40)	< 0.001
WBC (×10⁹/L), median (IQR)	5.80 (4.90 ~ 6.80)	6.00 (5.00 ~ 6.90)	5.70 (4.70 ~ 6.70)	< 0.001
Fglu (mmol/L), median (IQR)	4.80 (4.50 ~ 5.20)	4.90 (4.50 ~ 5.30)	4.80 (4.50 ~ 5.10)	< 0.01
HBAlc (%), median (IQR)	5.70 (5.50 ~ 6.00)	5.70 (5.50 ~ 6.00)	5.70 (5.50 ~ 6.00)	0.07
MCH (pg/cell), median (IQR)	30.30 (29.30 ~ 31.40)	30.50 (29.40 ~ 31.60)	30.2 (29.20 ~ 31.20)	< 0.001
IGF-1 (nmol/L), median (IQR)	15.00 (12.00 ~ 19.00)	16.00 (13.00 ~ 20.00)	14.00 (11.00 ~ 17.00)	< 0.001
Chol (mmol/L), median (IQR)	5.60 (4.80 ~ 6.40)	5.20 (4.50 ~ 6.00)	5.90 (5.20 ~ 6.70)	< 0.001
Note: TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Rtin, ferritin; hsCRP, high-sensitivity C-reactive protein; Cfib, fibrinogen; DHEA, dehydroepiandrosterone; Hgb, haemoglobin; WBC, white blood cell; Fglu, fasting blood glucose; HBAlc, glycosylated haemoglobin; MCH, mean corpuscular haemoglobin; IGF-1, insulin-like growth factor 1; Chol, cholesterol.

Cross-sectional associations between blood factors and frailty

All blood factors at baseline were included in multivariate logistic regression except cholesterol and low-density lipoprotein due to their high VIF value. In the multivariate logistic regression, 8 blood factors were statistically significant. Specifically, higher MCH (OR: 1.083, 95%CI: 1.004 ~ 1.168), TG (OR: 1.430, 95%CI: 1.080 ~ 1.893), hsCRP (OR: 1.135, 95%CI: 1.036 ~ 1.244) and WBC (OR: 1.206, 95%CI: 1.055 ~ 1.380) were associated with increased risk of frailty. Elevated concentrations of high density lipoprotein (HDL) (OR: 0.534, 95%CI: 0.290 ~ 0.982), ferritin (OR: 0.998, 95%CI: 0.9955 ~ 0.9998), DHEA (OR: 0.760, 95%CI: 0.640 ~ 0.903) and Hgb (OR: 0.695, 95%CI: 0.578 ~ 0.836) were associated with lower odds ratio of frailty (Table 2). The logistic regression model with these 8 significant blood factors had an area under curve (AUC) of 0.758, higher than that in models with any single blood factor (Supplementary Table 3, Supplementary Fig. 1).

Table 2

Multivariate logistic regression on blood factors and frailty
Blood factors	OR	P	95% CI for OR
Cfib (g/L)	1.125	0.59	0.731 ~ 1.733
HDL (mmol/L)	0.534	< 0.05	0.290 ~ 0.982
TG (mmol/L)	1.430	< 0.05	1.080 ~ 1.893
Rtin (ng/ml)	0.998	< 0.05	0.9955 ~ 0.9998
hsCRP (mg/L)	1.135	< 0.01	1.036 ~ 1.244
DHEA (umol/L)	0.760	< 0.01	0.640 ~ 0.903
IGF-1 (ng/ml)	1.024	0.17	0.990 ~ 1.059
Fglu (mmol/L)	0.953	0.74	0.719 ~ 1.264
Hgb (g/dL)	0.695	< 0.001	0.578 ~ 0.836
HBAlc (%)	1.202	0.44	0.752 ~ 1.920
WBC (×10⁹/L)	1.206	< 0.01	1.055 ~ 1.380
MCH (pg/cell)	1.083	< 0.05	1.004 ~ 1.168
Note: The analysis was controlled for age and sex; Cfib, fibrinogen; HDL, high-density lipoprotein; TG, triglyceride; Rtin, ferritin; hsCRP, high-sensitivity C-reactive protein; DHEA, dehydroepiandrosterone; IGF-1, insulin-like growth factor 1; Fglu, fasting blood glucose; Hgb, haemoglobin; HBAlc, glycosylated haemoglobin; WBC, white blood cell; MCH, mean corpuscular haemoglobin.

Blood factors and frailty development

The participants had a median follow-up time of 9.8 (IQR: 6.1 ~ 10.1) years. To analyse the longitudinal associations between baseline blood factors and frailty, survival analyses were performed. Results showed that higher baseline levels of TG (HR: 1.47, 95%CI: 1.23 ~ 1.75) and WBC (HR: 1.16, 95%CI: 1.05 ~ 1.28) were linked to a greater risk of developing frailty within 10 years, while the baseline HDL (HR: 0.58, 95%CI: 0.38 ~ 0.90) level demonstrated a reversed association (Table 3). The C-index of this CoxPH model was 0.6782.

Table 3

Results of CoxPH model on blood factor levels and frailty occurrence
Variables	HR	95%CI	P
MCH (g/L)	0.99	0.92 ~ 1.06	0.80
TG (mmol/L)	1.30	1.07 ~ 1.59	< 0.01
HDL (mmol/L)	0.58	0.38 ~ 0.90	< 0.05
Rtin (ng/ml)	0.999	0.997 ~ 1.000	0.18
hsCRP (mg/L)	1.06	1.00 ~ 1.12	0.07
DHEA (umol/L)	0.99	0.89 ~ 1.09	0.78
Hgb (g/dL)	0.92	0.80 ~ 1.05	0.21
WBC (×10⁹/L)	1.16	1.06 ~ 1.28	< 0.01
Age	1.07	1.04 ~ 1.10	< 0.001
Sex
Male	Reference	Reference	Reference
Female	1.85	1.29 ~ 2.65	< 0.01
Note: The C-index of this CoxPH model was 0.6782; MCH, mean corpuscular haemoglobin; TG, triglyceride; HDL, high-density lipoprotein; Rtin, ferritin; hsCRP, high-sensitivity C-reactive protein; DHEA, dehydroepiandrosterone; Hgb, haemoglobin; WBC, white blood cell.

Based on the number of abnormal baseline blood factor levels (Supplementary Fig. 2), we further classified the participants into five subgroups, i.e., groups with no (group 0), one (group 1), two (group 2), three (group 3), four and more abnormal factors (group 4) and performed an extra CoxPH analysis. The CoxPH model showed that participants with more abnormal factors at baseline would experience a higher frailty hazard ratio within 10 years. Specifically, with group 0 as the reference group, group 2 (HR: 1.85, 95%CI: 1.06 ~ 3.25), group 3 (HR: 2.43, 95%CI: 1.36 ~ 4.32), group 4 (HR: 4.13, 95%CI: 2.24 ~ 7.62) all demonstrated a higher risk of developing frailty over the year (Table 4).

Table 4

Results of CoxPH model on abnormal blood factor number and frailty occurrence
Variables	HR	95%CI	P
Abnormal Blood factor count
0	Reference	Reference	Reference
1	1.14	0.64 ~ 2.03	0.65
2	1.85	1.06 ~ 3.25	< 0.05
3	2.42	1.36 ~ 4.32	< 0.01
>=4	4.13	2.24 ~ 7.62	< 0.001
Age	1.07	1.05 ~ 1.10	< 0.001
Sex
Male	Reference	Reference	Reference
Female	1.66	1.25 ~ 2.20	< 0.001
Note: The C-index of this CoxPH model was 0.6818.

Sex-specified sensitivity analysis

Considering that the concentrations of some blood factors are sex-specific, we further performed sensitivity analyses by sex and identified four blood factors that were associated with frailty in each sex group.

In older men, TG (OR: 1.536, 95%CI: 1.035 ~ 2.280) and ferritin (OR: 0.996, 95%CI: 0.993 ~ 0.9996) were significantly correlated with frailty (Supplementary Table 4). A combination of these two blood factors yielded an AUC of 0.695 with a sensitivity of 0.674 and a specificity of 0.693 (Supplementary Table 5). The AUC of the model with two factors combined was higher than that in models with any single factor (Supplementary Fig. 3). The CoxPH analysis showed that TG (HR: 1.46, 95%CI: 1.12 ~ 1.90) and ferritin (HR: 0.997, 95%CI: 0.994 ~ 0.999) were all associated with the occurrence of frailty within 10 years (Supplementary Table 6).

In older women, MCH (OR: 1.258, 95%CI: 1.079 ~ 1.467), DHEA (OR: 0.639, 95%CI: 0.482 ~ 0.846), Hgb (OR: 0.575, 95%CI: 0.448 ~ 0.737), hsCRP (OR: 1.130, 95%CI: 1.003 ~ 1.273) and WBC (OR: 1.235, 95%CI: 1.042 ~ 1.467) were significantly correlated with frailty (Supplementary Table 4). A combined model with these five factors demonstrated an AUC of 0.744, better than that in models with a single blood factor (Supplementary Table 5, Supplementary Fig. 4). The CoxPH result showed that older women with a higher baseline WBC (HR: 1.25, 95%CI: 1.11 ~ 1.41) and hsCRP (HR: 1.08, 95%CI: 1.01 ~ 1.16) level were associated with greater risk of developing frailty in 10 years (Supplementary Table 6).

This study identified 8 out of 14 routine blood factors that were cross-sectionally associated with frailty in older adults based on a nation-wide ageing cohort in the UK. These identified frailty-related blood factors were involved in areas of inflammation, nutrition and metabolism, and demonstrated good performance in frailty detection at a cross-sectional level. Survival analysis further showed that older adults with two or more abnormal blood factors were more likely to develop frailty over a 10-year period compared to their peers without abnormal blood factors.

Our study showed that some inflammatory factors (i.e., hsCRP and WBC) were highly correlated with frailty. Extensive studies have reported the close association between inflammation and frailty. Douglas et.al. have pointed out that inflammation is significantly associated with frailty in men without cardiovascular disease (CVD)³⁰. A meta-analysis reported that frail population have higher levels of CRP, WBC and fibrinogen compared to people who were robust in cross-sectional studies³¹. As hsCRP and WBC are common indicators of inflammation³², our results have further confirmed the critical role of inflammation in frailty. Studies have found that higher levels of inflammatory markers are associated with greater losses of muscle strength and mass, resulting in reduced physical activity in older adults^33,34 and consequently leading to frailty.

Our results identified decreased blood ferritin, Hgb and MCH as risk factors for frailty. Hgb and MCH are common indices for anaemia which has a prevalence from 21–38% among older adults in different populations^35,36. The presence of anaemia among older adults has been associated with increased risk of frailty in cohorts³⁷. Moreover, as ferritin is a blood protein for iron storage, low ferritin level suggests an iron deficiency in the body, which can finally cause iron-deficiency anaemia. In respect of physical function, iron deficiency has also been linked to muscle strength and function declines in hospitalized older patients³⁸. When providing older adults with iron supplementation for six months, their grip strength and walking speed were largely improved³⁹. Additionally, iron status can also connect with frailty via its association with inflammatory markers (e.g., CRP⁴⁰ and IL-6⁴¹). Since iron, Hgb and MCH deficiencies are indicative of malnutrition, our findings implied the importance of healthy diet, particularly sufficient iron intake, in combating frailty.

The identification of TG and HDL as frailty-related factors indicates the role of lipid metabolism in frailty. Abnormal lipid concentrations can promote the formation of atherosclerotic plaques which affects physical performance with restricted blood supply in muscles and finally leads to frailty¹⁶. Beside lipid metabolism, we also found that DHEA was associated with the development of frailty. As a precursor of sex hormone, DHEA can affect skeletal muscle mass and strength via its roles in muscle protein synthesis and lipid metabolism⁴². Decreased DHEA level has been related to frailty in older adults¹³ while DHEA therapy was reported to be helpful in improving physical performance⁴³.

Although many studies have examined frailty-related blood factors, few have reported the performance of these factors in frailty screening. Mitnitski et al.⁴⁴ utilized 40 biomarkers (FI-B) to assess frailty and achieved an AUC of 0.66. Besides the large number of biomarkers involved in Mitnitski’s model, some of the parameters were also not accessible by routine tests, impeding the model from practical use. Based on 21 routine blood factors and blood pressure, Howlett et al.⁴⁵ created an index of FI-lab for frailty detection and achieved an AUC of 0.72. In our study, we only included 8 routine blood factors and obtained an AUC of 0.758. Moreover, the number of abnormal blood factors was also longitudinally associated with the occurrence of frailty. Therefore, a model with blood factors identified in our study would be convenient for clinical practice in frailty monitoring.

A rising number of studies have reported sex differences in frailty prevalence with women being more likely to become frail than men⁴⁶. Similar prevalence was also found in our study. Additionally, we further identified different frailty-related blood factors in male and female groups, indicating sex-specific pathways in the development of frailty. The underlying mechanisms of sex differences in frailty are poorly understood. Hormone variations⁴⁷, epigenetic changes⁴⁸ and mitochondrial dysfunction⁴⁹ are suggested to be possible contributors. For instance, frailty is related to inhibited myogenesis while such process is associated with increased myostatin level in men and decreased IGF-1 level in women¹⁴. Of note, our findings showed that the model between male and female were totally different, indicating that frailty screening by sex-specific blood factors could be useful based on different purposes.

The large sample size from a nation-wide study is one of the strengths in this study. The sex-specific analysis is also a novel attempt for creating a more robust frailty detection system. Meanwhile, we should admit that due to data availability, this study failed to include some inflammatory biomarkers, such as IL-6¹⁰, tumour necrosis factor α (TNF-α)⁵⁰ and tumour necrosis factor β (TNF-β)⁵⁰, which are commonly reported in frailty association studies. However, we still found that inflammatory factors (i.e., WBC and hsCRP) were closely associated with frailty. Moreover, our findings were based on a UK cohort and one should be cautious when generalizing the results to other populations.

Routine blood factors involved in inflammation, nutrition and metabolism could be used for frailty screening in clinical practice. In the detection of frailty, inflammatory biomarkers are critical blood factors while indicators related to other biological processes are also required. Sex-specified blood factors may be needed in order to achieve better monitoring accuracy.

Conflict of interest

All authors declared no financial and personal conflicts of interests.

Funding: The work was supported by grants from the Fundamental Research Funds for the Central Universities (No. 20720220061).

Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. Mar 2 2013;381(9868):752-62. doi:10.1016/s0140-6736(12)62167-9
O'Caoimh R, Sezgin D, O'Donovan MR, et al. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing. Jan 8 2021;50(1):96-104. doi:10.1093/ageing/afaa219
McAdams-DeMarco M, Chu NM, Segev DL. Differences Between Cystatin C- and Creatinine-Based Estimated GFR-Early Evidence of a Clinical Marker for Frailty. Am J Kidney Dis. Dec 2020;76(6):752-753. doi:10.1053/j.ajkd.2020.07.010
Buta BJ, Walston JD, Godino JG, et al. Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments. Ageing Res Rev. Mar 2016;26:53-61. doi:10.1016/j.arr.2015.12.003
Apóstolo J, Cooke R, Bobrowicz-Campos E, et al. Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools. JBI Database System Rev Implement Rep. Apr 2017;15(4):1154-1208. doi:10.11124/jbisrir-2016-003018
Gonzalez L, Kassem M, Owora AH, et al. Frailty and Biomarkers of Frailty Predict Outcome in Veterans After Open and Endovascular Revascularization. J Surg Res. Nov 2019;243:539-552. doi:10.1016/j.jss.2019.06.040
Sur G, Floca E, Kudor-Szabadi L, Sur ML, Sur D, Samasca G. The relevance of inflammatory markers in metabolic syndrome. Maedica (Bucur). Mar 2014;9(1):15-8.
Samson LD, Buisman AM, Ferreira JA, et al. Inflammatory marker trajectories associated with frailty and ageing in a 20-year longitudinal study. Clin Transl Immunology. 2022;11(2):e1374. doi:10.1002/cti2.1374
Jiang S, Kang L, Zhu M, et al. A potential urinary biomarker to determine frailty status among older adults. Arch Gerontol Geriatr. May-Jun 2020;88:104038. doi:10.1016/j.archger.2020.104038
Semmarath W, Seesen M, Yodkeeree S, et al. The Association between Frailty Indicators and Blood-Based Biomarkers in Early-Old Community Dwellers of Thailand. Int J Environ Res Public Health. Sep 17 2019;16(18)doi:10.3390/ijerph16183457
Leng SX, Hung W, Cappola AR, Yu Q, Xue QL, Fried LP. White blood cell counts, insulin-like growth factor-1 levels, and frailty in community-dwelling older women. J Gerontol A Biol Sci Med Sci. Apr 2009;64(4):499-502. doi:10.1093/gerona/gln047
Doi T, Makizako H, Tsutsumimoto K, et al. Association between Insulin-Like Growth Factor-1 and Frailty among Older Adults. J Nutr Health Aging. 2018;22(1):68-72. doi:10.1007/s12603-017-0916-1
Voznesensky M, Walsh S, Dauser D, Brindisi J, Kenny AM. The association between dehydroepiandosterone and frailty in older men and women. Age Ageing. Jul 2009;38(4):401-6. doi:10.1093/ageing/afp015
Chew J, Tay L, Lim JP, et al. Serum Myostatin and IGF-1 as Gender-Specific Biomarkers of Frailty and Low Muscle Mass in Community-Dwelling Older Adults. J Nutr Health Aging. 2019;23(10):979-986. doi:10.1007/s12603-019-1255-1
Zaslavsky O, Walker RL, Crane PK, Gray SL, Larson EB. Glucose Levels and Risk of Frailty. J Gerontol A Biol Sci Med Sci. Sep 2016;71(9):1223-9. doi:10.1093/gerona/glw024
Liu Z, Burgess S, Wang Z, et al. Associations of triglyceride levels with longevity and frailty: A Mendelian randomization analysis. Sci Rep. Jan 30 2017;7:41579. doi:10.1038/srep41579
Steinmeyer Z, Delpierre C, Soriano G, et al. Hemoglobin concentration; a pathway to frailty. BMC Geriatr. Jun 11 2020;20(1):202. doi:10.1186/s12877-020-01597-6
Vaes AMM, Brouwer-Brolsma EM, Toussaint N, et al. The association between 25-hydroxyvitamin D concentration, physical performance and frailty status in older adults. Eur J Nutr. Apr 2019;58(3):1173-1181. doi:10.1007/s00394-018-1634-0
Picca A, Calvani R, Cesari M, et al. Biomarkers of Physical Frailty and Sarcopenia: Coming up to the Place? Int J Mol Sci. Aug 6 2020;21(16)doi:10.3390/ijms21165635
Graig R, Deverill C, Pickering K. Quality control of blood, saliva and urine analytes. 2006;
Wang ZD, Yao S, Shi GP, et al. Frailty index is associated with increased risk of elevated BNP in an elderly population: the Rugao Longevity and Ageing Study. Aging Clin Exp Res. Feb 2020;32(2):305-311. doi:10.1007/s40520-019-01189-4
Schoufour JD, Erler NS, Jaspers L, et al. Design of a frailty index among community living middle-aged and older people: The Rotterdam study. Maturitas. Mar 2017;97:14-20. doi:10.1016/j.maturitas.2016.12.002
Sibille KT, McBeth J, Smith D, Wilkie R. Allostatic load and pain severity in older adults: Results from the English Longitudinal Study of Ageing. Exp Gerontol. Feb 2017;88:51-58. doi:10.1016/j.exger.2016.12.013
Clinic M. High cholesterol-diagnosis&treatment. https://www.mayoclinic.org/diseases-conditions/high-blood-cholesterol/diagnosis-treatment/drc-20350806.
Kadoglou NPE, Biddulph JP, Rafnsson SB, Trivella M, Nihoyannopoulos P, Demakakos P. The association of ferritin with cardiovascular and all-cause mortality in community-dwellers: The English longitudinal study of ageing. PLoS One. 2017;12(6):e0178994. doi:10.1371/journal.pone.0178994
Jackowska M, Kumari M, Steptoe A. Sleep and biomarkers in the English Longitudinal Study of Ageing: associations with C-reactive protein, fibrinogen, dehydroepiandrosterone sulfate and hemoglobin. Psychoneuroendocrinology. Sep 2013;38(9):1484-93. doi:10.1016/j.psyneuen.2012.12.015
Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Murphy WG. The sex difference in haemoglobin levels in adults - mechanisms, causes, and consequences. Blood Rev. Mar 2014;28(2):41-7. doi:10.1016/j.blre.2013.12.003
Khera A, McGuire DK, Murphy SA, et al. Race and gender differences in C-reactive protein levels. J Am Coll Cardiol. Aug 2 2005;46(3):464-9. doi:10.1016/j.jacc.2005.04.051
McKechnie DGJ, Patel M, Papacosta AO, et al. Associations between inflammation, coagulation, cardiac strain and injury, and subclinical vascular disease with frailty in older men: a cross-sectional study. BMC Geriatr. May 9 2022;22(1):405. doi:10.1186/s12877-022-03106-3
Soysal P, Stubbs B, Lucato P, et al. Inflammation and frailty in the elderly: A systematic review and meta-analysis. Ageing Res Rev. Nov 2016;31:1-8. doi:10.1016/j.arr.2016.08.006
Cheng Z, He D, Li J, Wu Q, Liu Z, Zhu Y. C-reactive protein and white blood cell are associated with frailty progression: a longitudinal study. Immun Ageing. Jun 3 2022;19(1):29. doi:10.1186/s12979-022-00280-1
Cesari M, Penninx BW, Pahor M, et al. Inflammatory markers and physical performance in older persons: the InCHIANTI study. J Gerontol A Biol Sci Med Sci. Mar 2004;59(3):242-8. doi:10.1093/gerona/59.3.m242
Visser M, Pahor M, Taaffe DR, et al. Relationship of interleukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: the Health ABC Study. J Gerontol A Biol Sci Med Sci. May 2002;57(5):M326-32. doi:10.1093/gerona/57.5.m326
Bach V, Schruckmayer G, Sam I, Kemmler G, Stauder R. Prevalence and possible causes of anemia in the elderly: a cross-sectional analysis of a large European university hospital cohort. Clin Interv Aging. 2014;9:1187-96. doi:10.2147/cia.S61125
Karoopongse E, Srinonprasert V, Chalermsri C, Aekplakorn W. Prevalence of anemia and association with mortality in community-dwelling elderly in Thailand. Sci Rep. Apr 30 2022;12(1):7084. doi:10.1038/s41598-022-10990-7
Lee CT, Chen MZ, Yip CYC, Yap ES, Lee SY, Merchant RA. Prevalence of Anemia and Its Association with Frailty, Physical Function and Cognition in Community-Dwelling Older Adults: Findings from the HOPE Study. J Nutr Health Aging. 2021;25(5):679-687. doi:10.1007/s12603-021-1625-3
Neidlein S, Wirth R, Pourhassan M. Iron deficiency, fatigue and muscle strength and function in older hospitalized patients. Eur J Clin Nutr. Mar 2021;75(3):456-463. doi:10.1038/s41430-020-00742-z
Selvi Öztorun H, Çınar E, Turgut T, et al. The impact of treatment for iron deficiency and iron deficiency anemia on nutritional status, physical performance, and cognitive function in geriatric patients. Eur Geriatr Med. Aug 2018;9(4):493-500. doi:10.1007/s41999-018-0065-z
McSorley ST, Jones I, McMillan DC, Talwar D. Quantitative data on the magnitude of the systemic inflammatory response and its relationship with serum measures of iron status. Transl Res. Oct 2016;176:119-26. doi:10.1016/j.trsl.2016.05.004
Wieczorek M, Schwarz F, Sadlon A, et al. Iron deficiency and biomarkers of inflammation: a 3-year prospective analysis of the DO-HEALTH trial. Aging Clin Exp Res. Mar 2022;34(3):515-525. doi:10.1007/s40520-021-01955-3
Sato K, Iemitsu M. The Role of Dehydroepiandrosterone (DHEA) in Skeletal Muscle. Vitam Horm. 2018;108:205-221. doi:10.1016/bs.vh.2018.03.002
Singh M, Alexander K, Roger VL, et al. Frailty and its potential relevance to cardiovascular care. Mayo Clin Proc. Oct 2008;83(10):1146-53. doi:10.4065/83.10.1146
Mitnitski A, Collerton J, Martin-Ruiz C, et al. Age-related frailty and its association with biological markers of ageing. BMC Med. Jul 13 2015;13:161. doi:10.1186/s12916-015-0400-x
Howlett SE, Rockwood MR, Mitnitski A, Rockwood K. Standard laboratory tests to identify older adults at increased risk of death. BMC Med. Oct 7 2014;12:171. doi:10.1186/s12916-014-0171-9
Kane AE, Howlett SE. Sex differences in frailty: Comparisons between humans and preclinical models. Mech Ageing Dev. Sep 2021;198:111546. doi:10.1016/j.mad.2021.111546
Gurvich C, Thomas N, Kulkarni J. Sex differences in cognition and aging and the influence of sex hormones. Handb Clin Neurol. 2020;175:103-115. doi:10.1016/b978-0-444-64123-6.00008-4
Sampathkumar NK, Bravo JI, Chen Y, et al. Widespread sex dimorphism in aging and age-related diseases. Hum Genet. Mar 2020;139(3):333-356. doi:10.1007/s00439-019-02082-w
Kristensen TN, Loeschcke V, Tan Q, Pertoldi C, Mengel-From J. Sex and age specific reduction in stress resistance and mitochondrial DNA copy number in Drosophila melanogaster. Sci Rep. Aug 23 2019;9(1):12305. doi:10.1038/s41598-019-48752-7
Nascimento CMC, Zazzetta MS, Gomes GAO, et al. Higher levels of tumor necrosis factor β are associated with frailty in socially vulnerable community-dwelling older adults. BMC Geriatr. Nov 6 2018;18(1):268. doi:10.1186/s12877-018-0961-6

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Editorial decision: Major revisions
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Frailty detection with routine blood tests: based on the English longitudinal study of ageing (ELSA)

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Abstract

Purpose

Methods

Results

Conclusions

Figures

Introduction

Method

Study design and baseline population

Blood tests

Frailty evaluation

Statistical analysis

Results

Baseline characteristics of participants

Cross-sectional associations between blood factors and frailty

Blood factors and frailty development

Sex-specified sensitivity analysis

Discussion

Conclusions

Declarations

References

Supplementary Files

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Version 1