A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments

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EURGER-909; No. of Pages 11 European Geriatric Medicine xxx (2017) xxx–xxx

Available online at

ScienceDirect www.sciencedirect.com

Research paper

A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments P. Ramakrishnan a,b, N. Alyousefi a, P.S. Abdul-Rahman b, S.B. Kamaruzzaman a,c, A.V. Chin a,c, M.P. Tan a,c,* a b c

Ageing and Age-Associated Disorders Research Group, Health and Translational Medicine Cluster, University of Malaya, Kuala Lumpur, Malaysia Department of Molecular Medicine, Faculty of Medicine, University of Malaya, Malaysia Divisions of Geriatric Medicine, Faculty of Medicine, University of Malaya, Malaysia

A R T I C L E I N F O

A B S T R A C T

Article history: Received 21 April 2017 Accepted 17 July 2017 Available online xxx

The approach to correlate frailty status with potential biomarkers has been generating increasing interest. However, there is currently no standardised definition or agreed biomarker for frailty. Hence, we conducted a systematic review on biomarkers evaluated in the published literature in relation to existing accepted measurements of frailty. The databases PUBMED, EMBASE, Web of Science and Science Direct were searched systematically for articles published from 2009 until July 2017. We included studies on frailty and associated biomarkers among individuals aged 65 years and older. Articles were reviewed by two reviewers independently. We identified 486 titles with 40 papers retained for final review after removal of duplicates and exclusion after the title, abstract and full-text review stages. Large variations in frailty measures and reported biomarkers were present in the published literature. Twentysix articles recruited subjects from community-dwelling older individuals and 33 used the Fried’s criteria. Of 11 studies, which evaluated Interleukin-6 (IL-6) against the Fried criteria, nine studies showed significant associations. Nearly all studies evaluating tumour necrosis factor-a, fibrinogen and C-reactive protein against Fried and Rockwood phenotypes showed positive associations. A large number of protein, nutritional, endocrine and genetic markers have been found to be associated with frailty defined with Fried, Rockwood and several other criteria, but only in isolated studies. The identification of potential biomarkers should be conducted with detailed knowledge of potential mechanistic pathways. It is likely that concurrent usage of clinical and biomarkers will be the favoured approach to the identification and management of frailty in the near future.  C 2017 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved.

Keywords: Aged Frailty Biomarkers Sarcopenia

1. Background Many theoretical and operational definitions for the frailty have now been determined. The concept of frailty as a syndrome can be defined as a state of vulnerability to stressors resulting from a decrease in functional reserve across multiple systems. Chronological age is not the best determinant for predicting frailty in older populations, and older adults represent a highly heterogenous group of individuals who will not all turn frail [1]. Parallel to this, frailty should not be considered synonymous with comorbidity and disability but instead represents the phenomenon of accelerated ageing, though among the extreme elderly this concept could be debated.

* Corresponding author. Department of Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia. E-mail address: [email protected] (M.P. Tan).

The Fried’s criteria or Fried’s frailty phenotype is the most widely accepted and applied frailty criteria. The frailty phenotype is defined as the presence of three of the five criteria of unintentional weight loss, weakness, self-reported exhaustion, slowness of gait and low physical activity. The term pre-frail is used to describe the intermediate group and individuals who do not possess any of the above symptoms are considered robust. The Fried’s criteria, however, assesses physical frailty alone [2]. The Rockwood’s Frailty Index (FI) on the other hand, has adopted an entirely holistic approach by considering a large number of parameters within functional, cognitive, as well as physical domains with a minimal number of forty parameters required to contribute towards a meaningful score. The FI score is then calculated by dividing the number of variables present by the total number of variables included. However, this approach has been found to be time consuming and is therefore not widely used clinically [3,4]. Instead, Rockwood and his co-researchers have developed a clinical frailty scale, by which the physician is required

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Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010

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to determine the frailty level of the patient by clinical observation alone, based on a seven-point scale [5]. The British Frailty Index (BFI) is an alternative frailty scoring system made up of seven domains, which provides a greater variance in the distribution of scores compared to the FI [6]. While the various published assessment tools are all useful the identification and measurement of frailty, clear understanding of the pathways and aetiological factors associated with frailty remains elusive. Such knowledge on the processes underlying frailty is vital for the detection, prevention and potential reversal of frailty. Biomarker research has shown promise in providing an insight into the ageing process at a molecular level. Seven proteins associated with the ageing process have been identified. These include inter-alpha trypsin inhibitor heavy chain H4, kininogen, apolipoprotein A4 and haptoglobin which are involved in metabolic pathways affected by the ageing process[7]. Similar biomarkers could therefore also determine factors that contribute to frailty in older adults, and add to the accuracy of prediction of survival. To understand the relevance and extent of the research published in the area of biomarkers for frailty we conducted a systematic review of the literature to identify and rationalize studies, which have compared laboratory biomarkers to recognised operational measures of frailty among older adults. 2. Methods The objective of this systematic review was defined using the participants, interventions, comparisons, outcomes and study (PICOS) design framework. The PUBMED, EMBASE, Web of Science, and Science Direct databases were searched for articles, published from 1 January 2009 until 30 June 2017, relevant to the topic. Articles with titles and keywords containing the search terms [‘‘frailty’’ OR ‘‘frail’’ OR ‘‘functional-impairment’’] AND [‘‘biomarkers’’ OR ‘‘biological markers’’ OR ‘‘laboratory markers’’ and ‘‘clinical markers’’ OR ‘‘clinical assessment’’] were identified. The search strategy is described using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement protocol [8] (Fig. 1). 2.1. Search criteria Research articles included in this review were selected if they met the following criteria: English language journal articles and studies including human subjects aged 65 years and above. Studies on physical and biochemical biomarkers or either were included since frailty research had included indicators from both types of analysis. Studies were excluded if the articles were only related to medical complications associated with ageing such as kidney failure, stroke, heart problem, cognitive impairment, functional decline and infections and did not report clinical frailty markers. Our study selection was not limited to study type. We also did not limit our search to biomarker research alone and had included the studies that reported clinical assessments. However, studies which did not include the evaluation of biochemical or laboratory markers were excluded. Articles were independently screened by two researchers. Any disagreements between them were settled by consensus. Similarly, data extraction was performed using a standardized data extraction form and disagreements were addressed by discussion. 3. Results 3.1. Selection process The initial database search (Fig. 1) yielded 439 potentially relevant articles. Two hundred and fifty-five records remained after removal of duplicates. The abstracts of the 114 articles

Fig. 1. PRISMA flow diagram.

retained after the title screen were then reviewed. Forty-two articles were excluded after the abstract review stage. The full-text articles of 72 research papers were included in the final review. Thirty-five articles were eventually selected and provided the basis for writing this systematic literature review including two ongoing studies published as an abstract and protocol paper respectively [9,10]. 3.2. Characteristics of included studies The findings from the final 40 articles which had evaluated a total of 51,440 (excluding one study which included in excess of 75,000 participants) participants are summarized in Tables 1 to 3. Table 1 contains studies which utilized the Fried’s criteria alone. Table 2 contains studies which used modified Fried’s criteria in isolation and studies which utilized Fried or modified Fried’s with other measures. Table 3 contains all the other studies which used either the Frailty Index or other frailty scales and measures. Of the

Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010

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EURGER-909; No. of Pages 11 P. Ramakrishnan et al. / European Geriatric Medicine xxx (2017) xxx–xxx Table 1 Characteristics of studies using the Fried’s criteria alone. References

Biomarkers

Study design

Inflammatory markers with or with other biomarkers IL-6 and/or CRP (to determine Cross sectional [16] inflammatory related diseases)

Study population

Results

Community dwelling elderly women. n = 620 Age = 70–79 y Mental health institutes & primary care n = 366 Mean age = 70.8

Increase in one inflammatory related disease, associated with 2-fold increased probability of being frail [prevalence ratio = 2.28, 1.81–2.87] Performance based criteria Handgrip strength: CRP (P = 0.002), IL-6 (P < 0.001) Gait speed: NGAL P = 0.02 Physical frailty phenotype not associated with inflammatory markers sICAM-1: stepwise increase from robust, pre-frail and frail elderly (P < 0.001) Frail vs non-Frailty: log ("IL-6) [OR(95%CI) = 1.54(1.07–2.20)] log("ICAM) [OR = 1.44(1.09–1.91)] Frail: " transferrin (P < 0.001), " fibrinogen (P < 0.0001), " IL-6 (P = 0.0035)

[30]

CRP, IL-6, NGAL

Cross sectional

[30]

sICAM-1, IL-6

Cross sectional

Community dwelling n = 946 Mean age = 65.5y

[24]

IL-6, transferrin, fibrinogen, haptoglobin.

Cross sectional

[38]

24 serologic markers (monocyte, T-cell, B-cell activation)

Cross-sectional

[26]

IL-6, CRP, TNFR2, Isoprostanes, LpPLA2 mass & activity, Osteoprotegerin, ICAM1, MCP-1, P-selectin

Prospective

Independent living retirement community n = 65 Mean age = 80.6 HIV2 n = 207 HIV+ n = 714 Community dwelling n = 1919 Mean age (Frail/Pre-frail/NonFrail) = (77/72/69)

[18]

Insulin, FFA, ghrelin, GLP-1, GH, IGF-1, IL-6, adiponectin, resistin, leptin. cf-DNA, IL-6, IL-10, CRP, unmethylated cf-DNA, mt-DNA

Prospective

Community dwelling women Age = 84–93 y

Prospective

Community dwelling Nanogenarians n = 144 Community dwelling n = 1478 Mean age = 75.3 y Community dwelling n = 2146 Mean ages (frail/non-frail): Men = 76.2/69.2 y Women = 76.7/69.7 y Community dwelling n = 433 Mean age = 77y

Frail: " cf-DNA (P = 0.002), unmethylated cf-DNA (P = 0.001), "mt-DNA (P = 0.029), " CRP (P < 0.001), " IL-6 (P = 0.004) Frail vs Non-Frail: " CRP [OR = 1.49 (1.05, 2.09)]

Community dwelling women. n = 326 Mean age = 74.1 y Community dwelling n = 144

Frailty: no related polymorphisms

[19]

[27]

hsCRP

Cross sectional

[21]

CRP, fibrinogen

Cross sectional

[23]

CRP, CML, albumin

Prospective

Genetic biomarkers [12] 56 SNPs in CNS, MTHFR, MTR, MRTT, TCN1 and TCN2 genes [32]

[37]

[35]

[10]

Cross sectional

HIV2- no significant difference HIV  Frail: "sCD14, sIL2Ralpha, sTNF-R2, IL-6, and TNF-a (0 < 0.002) CRP approached significance Frail [OR]: CRP [1.67(1.36,2.05)], IL-6 [2.01(1.63,2.48)], Isoprostanes [1.50(1.17,1.92)], LpPLA2 mass [1.38(1.10,1.72)], ICAM-1 [1.36(1.10,1.67)], MCP-1 [1.26(1.02-1.56)] Pre-Frail [OR]: CRP [1.24(1.12,1.38)], IL-6 [1.28(1.14,1.42)], TNF2 [1.40(1.20,1.63)], ICAM-1 [1.22(1.10,1.36)], MCP-1 [1.19(1.07,1.31)] Frail:# ghrelin (P < 0.05)

Women Frail: " CRP [OR = 1.27 (0.96,1.69)] and " fibrinogen [OR = 1.31(1.04,1.67)]

Frail vs non-Frail: CML in men [OR = 1.30 (1.14, 1.48)] " CML: # CRP (P = 0.01) CML: " in men with weakness (P = 0.04), low physical activity (P = 0.07) and exhaustion (P < 0.001)

Lymphocytes subsets CD3+, CD4+, CD8+, CD19+, CD3-, CD16 + 56+. DTP, UTP

Cross-sectional

Cross sectional

Community dwelling n = 811 Mean age = 74.3

hsCRP, HDL, Cholesterol, DHEAs SNPs [Rs1800629 (TNF), Rs1566729 (PTPRJ), Rs611646(ATM), Rs4646316 (COMT)] IGF1 signalling pathway, cell proliferation, gene expression,

Prospective

Community dwelling n = 3160 (Wave 2 & 4 ELSA) Mean age = 68.3 y (Wave 2),72.0 y (Wave 4)

Longitudinal

Community & clinic based elderly n = >75,000

Frailomic study-ongoing

Cross sectional case-control

Independent living retirement community & community dwelling older adults n = 8 (proteomics) n = 73 (ELISA) Mean age = 81 y

Proteomics Pre-frail: "haptoglobin, "transferrin, and " kininogen-1 variant isoform, #kininogen-1 variant isoform, #hemopexin precursor, #fibrinogen isoform, #leucine-rich alpha-2-glycoprotein 1 and # apolipoprotein E

Proteins/Glycoproteins [15] Plasma glycoproteins

Frail vs non-frail: # %CD3+ in  85 years (P < 0.05), # %CD4+ in 80–84 years (P < 0.05). #% CD19+ in 75–79 and 80–84 years old Highest UTP level: 64% (P = 0.025) frail, 36% (P = 0.038) prefrail. UTP: Pre-frail [OR = 0.85(0.76–0.96)], Frail [0.69(0.54–0.88)] "hsCRP, # Cholesterol, # HDL, #DHEAs Frail: Rs1800629 (P = 0.001), Rs1566729 (P = 0.006), Rs611464 (P = 0.027), and Rs4646316 (P = 0.01)

Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010

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4 Table 1 (Continued ) Biomarkers

Study design

Study population

Results

BDNF, GDNF, NGF

Quasi-experimental interventional

General community women n = 48, n = 20 (PTI) Mean age = 70.5 y (non-frail) Mean age = 72.5 y (pre-frail)

Pre-frail: #BDNF (P < 0.0001) After physical therapy intervention PTI: # TUG, " BDNF (P < 0.001)

Endocrine [39]

Leptin

Longitudinal (3.5 y follow-up)

Seniors-ENRICA cohort n = 1,753 Aged > 60 y

Frail: highest vs lowest Leptin tertile [OR = 2.2(1.47– 3.06)]

Nutritional [28]

25(OH)D

Cross sectional

Community dwelling n = 940 Mean age = 75.6 y

[29]

Bone turnover markers PINP, b-CTX, PTH, 25(OH)D

Cross sectional

[25]

Hb, Vit D, Albumin, Zinc, DHEA, Creatinine, D-Dimer, Cystatin-C, NT-proBNP

Prospective

Community dwelling women n = 592 Median age = 74 y In-patients (survivors after acute coronary syndrome)

25(OH)D levels > 15 ng/mL: less frequently frail/ prefrail than lower level Highest 25(OH)D showed lowest risk of frailty/ prefrailty. Frailty: "PINP [OR = 2.19(1.15–4.18)], # 25(OH)D [1.65(1.02–2.67)]. "PINP + # 25 (OH)D: [5.85(1.64-20.93). Frail: # Hb (P = 0.001), #DHEA (P = 0.006), #Vitamin D (P = 0.004), #Albumin (P = 0.007), #Zinc (P = 0.03), " Creatinine (P = 0.001), "D Dimer (P = 0.001), "Cystatin-C (P = 0.0001), "NT-proBNP (P = 0.0001)

References [14]

25(OH)D: 25-hydroxyvitamin D; ADT: androgen deprivation therapy; ATM: ataxia telangiectasia mutated; B-CTX: C-terminal telopeptide of type 1 collagen; BDNF: brain derived neurotrophic factor; cf-DNA: plasma cell free DNA; CI: confidence interval; CML: carboxymethyl-Lysin; COMT: catechol-O-methyltransferase; CRP: C-Reactive Protein; DHEAs: dihydroepiandrosterones; GDNF: glial-derived neurotrophic factor; Hb: haemoglobin; HDL: high density lipoprotein; hs-CRP: high sensitive C reactive protein; IL-6: interleukin 6; ICAM-1: intra-cellular adhesion molecule-1; MMA: methylmalonic acid; NGF: nerve growth factor; NGAL: neutrophil gelatinase-associated lipocalin; NT-proBNP: N-terminal pro-B-type natriuretic peptide; OR: odds ratio; PINP: N-terminal propeptide of type 1 procollagen; PTH: parathyroid hormone; PTI: physical therapy intervention; PTPRJ: protein tyrosine phosphatase receptor type J; sICAM-1: soluble Intercellular adhesive molecule-1;TNF: tumour necrosis factor; TNF-a: tumour necrosis factor alpha; TNFR2: tumour necrosis factor receptor 2.

diagnostic, staging, classification, prognostic indicator and prediction and monitoring of clinical response to treatment [50].

studies included, 37 were observational while two were interventional studies, one of quasi-experimental design, the other a pilot randomised-controlled trial and one ongoing multi-centre prospective study. Among the 37 observational studies, 18 were prospective while 19 were cross-sectional. Duration of follow-up of the studies included (where applicable) was between from 10 weeks and 10 years and the sample size of the selected study populations range from 48 to > 75,000 subjects. Overall, participants from the studies were recruited from the community (26), clinic/in patients/clinical research centre (10), assisted living or independent living retirement community (3) and institutionalized (1) settings. There were seven gender specific studies, six involving only women and one involving only men. Nine studies were conducted in disease specific populations, namely, HIV (2), hip fractures (1), colorectal cancer (1), breast cancer (1) and prostate cancer (1), acute coronary syndrome (1), cognitive impairment (1) and mental health institute (1). Fried’s criteria was the most widely used frailty measure in the research articles included in this review [9–40]. Among these, a few studies had used a modified Fried’s criteria by substituting weight loss with obesity [13], eliminating one criterion due to lack of information [41], and other modified versions [42,43]. Apart from Fried’s criteria, the FI with a different number of total variables [9,20,30,31,36,44–46] and the Comprehensive Geriatric Assessment (CGA) [43] were used. Other single measurement approaches to quantifying frailty had also been employed, including frailty phenotyping using cluster analysis [47], FRAIL scale instrument [9], Buchmann criteria [48], Balducci score and Leuven Oncogeriatric Frailty Score (LOFS) [49].

Inflammatory biomarkers analyses were the most frequently reported biomarkers among the articles included in this review. Three studies [16,21,43] focused solely on these biomarkers whereas other articles [13,15,18,19,22–24,26,30,33,34,42,48,49] incorporated additional types of indicators (genetic, nutrition, glycoproteins, adhesion molecules, immune markers and physical domains). IL-6 [16,19,24,26,30,33,38,40,43,49], TNF-a[42,43,48] and fibrinogen [15,21,24] were significantly associated or correlated with frailty status in several studies (Table 4). Gale reported that CRP levels were higher in frail women but not in men [21], one study [42] showed positive associations between frailty and IL-6, CRP, and TNF-a. Whitson reported lower CRP levels with higher Carboxymethyl-Lysine (CML) levels, whereby CML levels were significantly associated with frailty in men [23]. Similarly, high CRP levels were also significantly correlated with physical frailty indicators (handgrip strength) [30] and genetic variants [22]. Neutrophil gelatinase-associated lipocalin (NGAL) an interesting specific inflammatory marker was significantly associated with gait speed (a specific frailty criteria) in late life depression and lowgrade inflammation among the elderly [30]. A recent pilot study has revealed that a group of cytokines/chemokines predict frailty status by both Fried’s and Frailty Index significantly endorsing the hypothesis that systemic inflammation is involved in the biology of frailty [34].

3.3. Biomarkers

3.5. Genetic markers

A biological marker (biomarker) is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathological processes or pharmacologic responses to a therapeutic intervention [50]. Biomarkers can be proteins, deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or even metabolites [51]. The valuable applications of biomarkers in disease detection and monitoring of health status include:

Seven studies [12,19,20,22,35,44,47] provided some evidence on genetic frailty markers. This collectively contributed to a wide scope of information on the genetics of frailty. Higher methylmalonic acid (MMA) levels which are determined by TCN2 gene variants contributed to frailty progression, [12] while cf-DNA, unmethylated cf-DNA and mt DNA levels directly correlated with Fried’s frailty score [19]. The influence of genetic factors on frailty

3.4. Inflammatory markers

Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010

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Table 2 Studies using Fried’s or modified Fried’s with or without other frailty measures. References

Frailty measure

Modified Fried Alone Modified Fried’s [13] (replacing weight loss with obesity).

[40]

Modified Fried’s

[42]

Modified Fried’s

[41]

Modified Fried’s (minus weight loss criteria, frail = 2 criteria)

Biomarkers

Study design

Study Population

Results

IL-6, CRP, albumin, Hb, lipids, fasting glucose

Cross sectional case-control

Men on ADT are frailer (modified Fried’s) ADT subjects: " Frailty, # Hb, " IL-6, " CRP

IL-6, hs-CRP, sTNFR-1 & 2, DHEAs, testosterone, HOMA-IR, IGF-1, %CD4 + CD28-, %CD8 + CD29-, %CD4 + CD38 + DR+, %CD8 + CD38 + DR+ T cells IL-6, TNF-a, CRP, RBC, DHA

Cross-sectional Matched controls

Prostate cancer outpatients Group 1: on ADT n = 63, Mean age = 72.1 y Group 2: without ADT n = 71, Mean age = 70.5 y HIV infected frail: n = 155 HIV infected non-frail: n = 141 Non-HIV: n = 150

Randomised, control study on fish oil supplementation

Postmenopausal women n = 126 Mean age = 75

Cross sectional

Civilian noninstitutionalised. n = 4731 Mean age Frail= 73.1 Pre-Frail = 71.8 Non-Frail = 69.4

Frail: #RBC DHA (r = 0.242, P = 0.007); #DHA/AA (r = 0.254, P = 0.004) 6 months Fish Oil led to walking speed improvement (P = 0.038) explained by DHA/ AA (P = 0.01), TNF-a (P = 0.039), selenium (P = 0.031) Frail: # albumin (P = 0.002), # carotenoids (P = 0.01), # Se (P = 0.004)

Cross Sectional

Community dwelling n = 76 Mean age = 68

Albumin, folate, vitamin A, B12, C and E, carotenoids

Fried/Modified Fried with Other Frailty Scales [34] Fried’s + FI 81 candidate serological biomarkers

[9]

Fried’s + FI + FS

CRP, Vitamin B12, vitamin D, Cystatin-C, lutein, zeaxanthin

Cross Sectional (Ongoing Longitudinal study)

Community living elderly n = 4548

[43]

Modified Fried’s + CGA.

CRP, IL-6, TNF-a, D-dimer

Prospective observational

[36]

Fried’s+ FI-CD FI-B

Cohort study

Fried’s + FI

Frailty Index-Biomarker (FI-B) (Inflammatory, haematological, immunological, cell senescence, genetic, and epigenetic markers) HtrA1

In-patients (3 public hospitals). Colorectal cancer patients n = 187 Mean age = 80 Community dwelling N = 777 Mean Age = 85.5 y

NA

Geriatric outpatient Clinic n = 120 Mean Age = 75.4 y

Fried’s + FI + FS

10 mtDNA haplogroups

Prospective

Institutionalised elderly n: 700 Mean age: 85.5 Healthy older subjects n: 93 Mean age: 69 y

[ 31]

[20]

Frail: "IL-6, "hs-CRP, #testosterone, #DHEA No immunosenescence with frailty

8 serological biomarkers were associated with frailty (sgp130, I-309, MCP-1, IL-6R, IL-2Ra, BCA-1, RANTES, leptin) Pre-frail: " sgp130 (P < 0.05), " IL-2Ra (P < 0.05), " CD45RA + CD8 (P < 0.01) Frail: " I-309 (P < 0.05), # CD57 (P < 0.05), CD24-CD38 B cells (P < 0.05) " Lutein: Fried [RR = 0.59], FI [RR = 0.86], FS [RR = 0.53] " Cystatin-C: Fried [RR = 1.52], FI [RR = 1.16], FS [RR = 1.34] " Vitamin D: Fried [RR = 0.83] Findings imply increased Lutein and Vitamin D reduces risk of Frailty while increased Cystatin-C increased risk of frailty Frail: " CRP (P = 0.001), " IL-6 (P < 0.001), " TNF (CGA) (P = 0.001)

FI-B and FI were significantly but weakly correlated, but FIB and FI complement each other

Fried’s Frail: HtrA1 " 75.9 (67.4– 85.6) (P < 0.001) Frailty Index Frail: HtrA1 " 72.2 (63.4– 82.3) (P < 0.001) FI & FS: No significant association with mtDNA haplogroups

Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010

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6 Table 2 (Continued ) References

Frailty measure

Fried’s and Single Measures [17] Fried’s + HG + SPPB

Biomarkers

Study design

Study Population

Results

Cu, Zn

Cross sectional

Community or assisted living n = 144 Mean age = 77.1 y

High Cu/Zn: " frail (not significant) High Cu/Zn: # SPPB score (P = 0.014), # cholesterol (P = 0.041), # triglycerides (P = 0.001)

ADT: androgen deprivation therapy; CGA: comprehensive geriatric assessment; CRP: C-reactive protein; Cu: copper; DHA/AA: docasahexaenoic acid/arachidonic acid; DHEAs: dihydroepiandrosterones; FI: Rockwood’s frailty Index; FS: FRAIL scale; HG: handgrip; hs-CRP: high sensitive C reactive protein; HtrA1: serine protease A1; IL-6: interleukin 6; mt-DNA: mitochondrial DNA; PSA: prostate specific antigen; RBC: red blood cell; RR: risk ratio; SPPB: short physical performance battery; sTNFR: soluble receptros for TNF- a; TNF: tumour necrosis factor; TNF-a: tumour necrosis factor alpha; TNFR2: tumour necrosis factor receptor 2; Zn: Zinc.

differs with gender, [47] with a genetic background being more related to frailty conditions in men while environmental factors are a bigger influence on frailty in women. Frailty status measured by Rockwood’s and Fried’s is not associated with the mtDNA haplogroups [20]. Single nucleotide polymorphism (SNPs) of CRP significantly predicts lower handgrip strength and higher serum CRP levels in one study [22]. Of the genes involved in steroid hormone metabolism and inflammatory pathways investigated, four gene Interleukin-18 (IL-18), Interleukin-12(IL-12A), low density lipoprotein receptor-related protein-1(LRP) and selectinP (SELP) gene are associated with frailty status [44]. Subsequently, using the same population, another study was conducted using Fried’s frailty assessment to explore the same pathway as mention earlier and this revealed that Tumor Necrosis Factor (TNF), Protein Tyrosine Phosphatase (PTPRJ), Ataxia Telangiectasia Mutated (ATM) and Catechol-O-methyltransferase (COMT) genes are associated with frailty although none of these associations remained significant after adjustment for potential confounders. 3.6. Proteins/Glycoproteins markers Two studies provided information on glycoproteins [15,24] as biomarkers of frailty, whereby a preliminary study [15] was conducted in a small study using a proteomics approach to identify proteins of interest (kininogen-1-variant isoform, hemopexin, fibrinogen, leucine-rich alpha-2-glycoprotein 1 and apolipoprotein E) and further validation [24] in larger samples found that higher levels of transferrin, fibrinogen, and interleukin-6 were associated with frailty. A recent pilot study involving 12 participants employed proteomics technology to evaluate 226 proteins using the Clinical Frailty Scale. Thirty-one proteins increased with level of frailty, while Ig kappa chain V-III, WOL, COX7A2 and albumin reduced with increasing frailty [46]. Apart from exploring the prognostic role of typical biomarkers in the study of frailty, researchers of today are more endowed with sensitive novel proteins and molecules from pathways, which could lead to progression in the severity of frailty. Plasma brainderived neurotrophic factor (BDNF) is detected in all study subjects but is significantly higher in non-frail compared to pre-frail elderly women [14]. Cystatin C is a sensitive marker for renal dysfunctional, with increases in the levels of this protein also positively associated with frailty [9]. Additionally, this protein is valuable among survivors of acute coronary syndrome as a tool for frailty evaluation at hospital discharge [25]. Elevated levels of isoprostanes (biomarkers of oxidative stress) and lipoporotein-associated phospholipase A2 (LpPLA2) are associated with increased odds of frailty and slower gait speed [26]. In a population-based study conducted among Spanish women, increased levels of serum Nterminal propeptide of type 1 procollagen (PINP-serum bone metabolism marker) and reduced levels of 25(OH) D are associated with a five-fold increase in frailty risk [29]. High-temperature requirement serine protease A1 (HtrA1) is a protein involved in the

inflammatory process that inhibits signalling of the active transforming growth factor-b (TGF-b), responsible in immune homeostasis. Increases in HtrA1 levels are associated with increasing levels of frailty [31]. Another study, which evaluated inflammatory, related protein molecules including the soluble intercellular adhesive molecule-1 (sICAM-1) found a significant association between sICAM-1 and frailty independent of IL-6 in community dwelling older people in Taiwan [33]. Although frailty status does not significantly influence immunological parameters, a reference range for lymphocytes on older adults is established as a foundation for the expanding role of lymphocyte based frailty biomarkers [32]. 3.7. Endocrine Markers A recent study utilizing the Seniors-ENRICA cohort found a correlation between Leptin levels and frailty [39]. Another study found that FT3 correlated with frailty scores, and is able to identify the presence of frailty with 74% sensitivity and specificity among hip fracture patients recruited from the orthopaedic ward and control participants from outpatients [45]. The Insulin Growth Factor-1 (IGF-1) had been evaluated in four studies conducted in community-dwelling, cancer, HIV and dementia populations, with none of the studies demonstrating a significant relationship [18,40,48,49]. Dihydroepiandrosterone (DHEA) has been evaluated in four previous studies with three studies, a large community study with 3160 participants, a study of survivors of acute coronary syndrome and a study of HIV positive individuals utilizing the Fried’s or modified Fried’s criteria showing a reduction in DHEA with frailty [25,35,40]. One study evaluating DHEA in a dementia clinic setting found no significant association when compared with Buchmann’s criteria [48]. Other endocrine markers evaluated included carboxymethyl-Lysine and circulating energy metabolism hormones, of which both studies found significant associations between those hormones and frailty [18,23]. Serum CML was significantly associated with adverse outcomes only among men [23]. Frailty status was associated with altered energy metabolism in terms of lower levels of ghrelin with fasting and 120 minutes after an oral glucose challenge [18]. 3.8. Nutritional markers Nine studies [9,12,17,25,28,29,37,41,42] provided information on the relationship between nutritional status, either vitamin, micronutrients or supplementation, with frailty status. Low albumin level is present in frail older adults with significantly lower carotenoids and selenium compared to non-frail individuals [41]. Folate dependent 1-carbon metabolism does not play a role in frailty pathogenesis when analysed along with genes for vitamin B dependent metabolism [12]. The increase in copper zinc ratio (Cu/ Zn) indicates lower iron levels and this alters physical frailty phenotypes (muscle strength). Besides that, high Cu levels alone

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Table 3 Other Frailty Measures. References

Frailty measure

Biomarkers

Study design

Study population

Results

[45]

SHARE-FI

FT3

Cross-sectional

Hip Fracture Inpatients N = 62 Mean age = 79.9 y Older Outpatients n = 50 Mean age = 78.1 y

Frailty score correlated significantly with FT3 (r = 0.436, P < 0.001) FT3 < 2.3 pg/mL distinguish frail with 74% sensitivity, 74% specificity

[46]

CSHA-CFS

Proteomics

Case-control

Frail n = 6 (3 men, 3 women) Non-Frail n = 6 (3 men, 3 women) Aged  65 y

[44]

Rockwood’s FI

IL-18, IL-12A, LRP, SELP Genetic Markers (using Genotype data of 620 SNPs from ELSA DNA Repository)

Prospective

Community dwelling n = 3160 (Wave 2 ELSA) Mean age = 68.3 y(Wave 2)

[22]

HG

hs-CRP

Prospective

Community dwelling n = 472 Mean age = 73.8 y

226 proteins detected Frail: " 31 proteins (P00450, Q5T985, P08603, Q9UP60, P02790, P01009, Q9P173, P10643, D3DNU8, P04004, P02763, B4E1Z4, Q6MZV7, C9JV77, B4E1C2, Q86U78, P13671, P04003, G3V5I3, P01008, P01598, P10909, P00738, P01031, Q68CX6, P01777, Q68CK4 and Q6MZV6) (P < 0.05); #Ig kappa chain V-III region WOL, COX7A2 and albumin (P < 0.05) Increased FI: Rs360722 (IL-18) (P < 0.005); rs4679868(IL-12A), rs9852519 (IL-12A) and rs6131(SELP) (P < 0.01) Decreased FI: rs1799986 (LRP1)(P = 0.0065 Further 12 SNPs significant at P < 0.05 Haplotype (c-c-c-c-c): #b HG (P < 0.05), "b CRP (P < 0.001)

[49]

T/S ratio, IL-6, IGF-1, MCP-1, RANTES

Retrospective

[48]

Balducci score Leuven Oncogeriatric Frailty Score Buchmann’s criteria

IL-6, TNF-a, IGF-1, DHEAs Pro-inflammatory (high IL-6 or TNP- a) Endocrine deficient (low IGF-1 or DHEAs)

Prospective

[47]

MMSE, ADL, HG, SRHS

MZ, DZ and DZ same-sex

Prospective Cluster Analysis

Hospital (Cancer patients) n = 244 Median age = 76 y Tertiary memory clinic (Mild cognitive impairment & mild-moderate Alzheimer’s disease) n = 99 Mean age = 76.6 y Community dwelling elderly First sample (Danish 1905 cohort) n = 1345 Mean age = 93.2y Second sample [Longitudinal Study of Aging Danish Twins (LSADT) cohort] n = 2339 Mean age = 77.7 y

Balducci Frailty: IL-6 (P = 0.019) LOFS Frailty: IL-6 (P = 0.013) Baseline Frail: " TNF-a (P = 0.035) Frailty: Proinflammatory [OR = 4.99 (1.25–19.88)] 1 year Frailty: Proinflammatory [4.06 (1.09–15.10)] Mean distance for geriatric parameters was 11% in MZ than DZ twins (P = 0.038) Polychoric correlations higher among MZ and DZ for all age groups Data suggest a genetic influence on frailty variability

ADL: activity of daily living; CSHA-CFS: Chinese-Canadian Study of Health and Ageing Clinical Frailty Scale; CRP: C-reactive protein; Cu: copper; DHA/AA: docasahexaenoic acid/arachidonic acid; DHEA: dehydroepiandrosterone; DZ: dizygotic; FI: Rockwood’s Frailty Index; HG: handgrip; hs-CRP: high sensitive C reactive Protein; IL-6: interleukin 6; IGF-1: insulin-like growth factor 1; LRP: low density lipoprotein receptor-related protein 1; MCP-1: monocyte chemoattractant protein-1; MMSE: mini mental state examination; mt-DNA: mitochondrial DNA, MZ: homozygotic; SELP: selectin P; SHARE-FI: survey of health, ageing and retirement in Europe frailty instrument, SPPB: short physical performance battery; SRHS: self-reported health status; TNF: tumour necrosis factor; TNF-a: tumour necrosis factor alpha.

are associated with deficits in walking speed and the Get-Up-andGo test [17]. Four studies had evaluated Vitamin D levels, which was found to be lower in frail patients together with the decreased level of albumin and zinc [9,25,28,29]. An ongoing study has found that an increase in lutein is negatively associated with three frailty measures and higher levels of zeaxanthin are negatively associated with two frailty measures [9]. Urinary Total Polyphenols (UTPmarker of polyphenol intake) concentration is inversely associated with the frailty phenotype among community dwelling older populations in Italy. Dietary intake of polyphenols in terms of a polyphenol rich diet is associated with a lower risk of developing frailty [37].

4. Discussion Biomarker research has evolved rapidly since the 1980s with the increase in high throughput technology in biology. This systematic review has demonstrated that numerous biomarkers representative of a wide range of physiological systems have been compared against two main frailty assessments [52]. The use of a combination of physical domains and biomarkers in the assessments of frailty rather than focusing in either, may have an added advantage as both features are equally important in contributing to the frailty syndrome. This also clearly reflects the multifactorial origin of frailty. Inconsistencies have been demonstrated in

Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010

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Table 4 Total number of studies conducted on each biomarkers by frailty assessment tools. Biomarkers (no. of studies)

Frailty assessments Frieda

Inflammatory markers (22) L-6 CRP TNF-a Fibrinogen Chemokinesb NGAL Isoprostanes Osteopogerin Genetic markers (10) Heritability cf-DNA mt-DNA HtrA1 CRP gene polymorphism TCN2 Polymorphism SNPs (Proinflammatory) SNPs (Steroid hormone & Inflammation) Protein/Glycoprotein markers (12) BDNF GDNF NGF D-Dimer Haptoglobin Transferrin Kininogen Hemopexin Leucine rich alpha-2-glycoprotein Apolipoprotein E Plasma brain derived neurotrophic factor Cystatin-c NT-proBNP PINP ICAM-1 UTP DTP LpPLA2 P-Selectin Endocrine markers (7) Leptin FT3 IGF-1 DHEA Carboxymethyl-Lysine Circulating energy metabolism hormones Nutritional markers (8) Vitamin D Albumin Cu/Zn Vitamin B12/MMA Folate Lutein DHA Selenium Carotenoids Zeaxanthin

9/11 4/5 3/3 3/4 1/1 1/1 1/1 0/1

Rockwooda

CGA

Handgrip

1/1 1/1 1/1

1/1

SRHS

ADL

Others 2/3 1/1

1/1

1/1 1/1 0/1 1/1

MMSE

1/1

1/1

1/1

0/1 1/1

1/1 1/1 1/1 1/1 0/1 0/1 1/2 1/2 2/2 1/1 1/1 1/1 1/1 1/1 1/1 1/1 1/1 2/2 1/1 0/1 1/1 0/1

1/1

1/1

2/2

1/1

1/1 1/1 0/2 0/1

0/2 3/3 1/1 1/1 3/3 2/4 1/1 2/2 0/1

1/1 1/1

1/1

SPPB: 1/1

1/1

1/1 2/2 1/1 1/1

The numerator represents the number of studies, which reported a positive association with frailty while the denominator represents the number of studies, which evaluated the biomarker against the frailty assessment tool. Results of multiplex ELISA, proteomic and genomic studies are not included. CGA: comprehensive geriatric assessment; MMSE: mini-mental state examination; SRHS: self-rated health status; ADL: activites of daily living; IL-6: interleukin-6; CRP: C-reactive protein; TNF: tumour necrosis factor; NGAL: neurotrophil gelatinase-associated lipocalin; cf-DNA: plasma cell free DNA; mtDNA: mitochondrial DNA; HtrA1: serine protease A1; TCN: transcobalamin; SNP: single nucleotide polymorphism; BDNF: brain derived neurotrophic factor; GDNF: glial-derived neurotrophic factor; NT-proBNP: N-terminal pro-B-type natriuretic peptide; PINP: N-terminal propeptide of type 1 procollagen; ICAM-1: intra-cellular adhesion molecule-1; UTP: urinary total polyphenols; DTP: dietary total polyphenols; LpPLA2: lipoprotein-associated phospholipase; IGF: insuline growth factor; DHEA: dihydroepiandrosterone; Cu: copper; Zn: zinc; MMA: methylmalonic Acid; DHA: docasahexaenoic acid. a Frailty Index b Chemokines (Sgp130, I-309, MCP-1, BCA-1, RANTES, LEPTIN).

biomarkers that have been evaluated in several studies. However, the over-representation of positive findings suggests strong publication bias, which has been a feature of biomarker research. Alternatively, the underlying aetiology of frailty could indeed be extremely broad, and may be triggered by almost any insult, as

suggested by the multiple deficit approach taken by Rockwood’s frailty index [5]. Identification of biomarkers is now considered vital in addressing the ‘‘frailty epidemic’’, which has emerged with population ageing and improved treatment for chronic diseases

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among older adults. Accurate descriptions of the contribution of individual potential biomarkers towards the development of frailty should aide in the understanding of its underlying pathways and mechanisms. In addition, these biomarkers may act as convenient, objective measurements of frailty status and its severity as well as serve as potential therapeutic targets for the treatment of frailty. If a particular biomarker were to be analysed, it should be identified as a fit within the correct frailty mechanism or pathway level involving the particular biomarker [53]. The appropriate biomarker should preferably be easily obtainable such as routinely available laboratory parameters. Among the biomarkers evaluated in the literature, inflammatory biomarkers were most commonly assessed. Interleukin-6, as a marker of acute inflammation, has been most widely evaluated, with nearly all such studies comparing IL-6 with the Fried frailty phenotype. The findings have, however, been conflicting, which may reflect the high variability in participant selection between studies. Studies involving disease specific groups were more likely to demonstrate significant relationships between specific biomarkers than those involving general populations. This suggests the potential confounding effects of inflammation from underlying disease. For instance, NGAL, a marker of low-grade inflammation, was associated with the physical frailty dimension gait speed in late life depression among the elderly [30]. In another study which evaluated physical frailty among cognitively impaired older adults, increased TNF-a, a commonly used proinflammatory cytokine, was significantly raised among frail subjects at baseline and one-year follow-up [48]. The other inflammatory markers evaluated were fibrinogen and CRP, which in nearly all published studies, had demonstrated an association with frailty determined by both the Fried and Rockwood criteria. The role of these markers as independent biomarkers for frailty, however, is limited by the confounding effects of acute inflammation. CRP gene polymorphism is correlated with higher CRP levels and frailty determined using the single measure of lower handgrip strength [22]. Activation of the several cellular stress related pathways and an apoptotic pathways (transcriptomic analysis) determined by elevated total and unmethylated cf-DNA is also associated frailty, supporting the response to stressor hypothesis as the underlying mechanism of frailty [19]. Studies involving neuroendocrine markers, metabolic factors and SNPs should not end at identification level. Instead, the genetic biomarkers identified may help open up new opportunities for the next level of more specific biomarkers candidates. Further studies in molecular determinants of frailty are warranted, in particular, in candidate genes involved in the cholesterol transport and inflammatory pathways [44]. In addition to this, pathway analyses should be explored to understand complex interactions between discovered genes and proteins for a more comprehensive illustration of the frailty pathway. One of the more effective ways to prevent frailty development appears to be nutritional supplementation and consuming balanced foods. For instance, polyphenol-rich diets have demonstrated protective effects against the manifestation of frailty in older subjects [37]. The role macro- and micronutrients, hormones and energy metabolism as biomarkers of frailty has therefore been explored [18]. However, as only isolated observational studies have been conducted thus far, it would be important now to identify markers which are consistently altered across various populations. The evaluation of appropriate interventions to correct for defects in nutritional, hormonal or energy metabolism identified through the potential biomarker should also be considered. This would allow for an assessment of whether improvements in the markers measured corresponded with improvements in clinical markers.

9

The identification of conditions that are commonly associated with frailty allow for opportunistic screening. Subjects with underlying chronic medical conditions (cancer), for instance, could be targeted in screening for risk of developing frailty with initiation of potentially tailored interventions. Four studies had evaluated frailty among hospitalised patients [25,43,45,49]. The hospital environment could result in psychological fluctuations, loneliness and poor food intake, which lead to further reduced physical activity, and further exacerbate the risk of frailty above the effect of the actual physical illness [54]. Haemoglobin, Vitamin D and Cystatin-C levels, when incorporated in pre-discharge evaluation of survivors after acute coronary syndrome identifies those at risk of frailty allowing for targeted interventions post-discharge [25]. The other two studies were conducted among inpatients with colon and breast cancer which identified IL-6, CRP and TNF- a as potential frailty biomarkers [43,49]. The fourth and most recent study employed FT3 as a potential frailty biomarkers, but their controls were outpatients which cases were inpatients with hip fractures [45]. FT3 may also be a marker of acute illness. Their findings will, therefore, require further verification in better-controlled populations. All the hospital-based studies had also been conducted in disease specific populations, with studies involving the general older hospitalized population urgently required. Candidate markers for frailty in hospitalized older patients may potentially hold the key to frailty assessment among older hospitalized patients, to identify those most at risk of acute deterioration, reduced survival and poorer post-discharge outcomes. In addition to the lack of a standardised accepted definition of frailty, the pool of biomarkers suggested in the literature is also highly varied, and in most cases yet to be replicated outside its original population. Multifaceted analysis from a different type of tools will lead to heterogenous results which will limit the applicability of results to other populations. Majority of the studies were conducted in the continents of Europe and America and only five studies were based in the Asian region. Therefore, the pursuit of laboratory biomarkers to measure frailty still requires concerted efforts relevant to potential underlying mechanistic pathways, with widespread collaboration between clinical and scientific experts. The findings of the large-scale collaboration from the FRAILOMIC project will be a welcome addition to the current available literature [10]. 5. Conclusion This systematic review provides an overview of biomarkers assessed against clinical frailty measures. Thus far, inflammatory markers have been most widely studied against the Fried’s frailty phenotype, which have yielded conflicting results. Genetic, nutritional, endocrine, protein and haematological biomarkers have also been reviewed but, mainly in isolated studies. The applicability of available published evidence is, therefore, limited by the heterogeneity of frailty definitions, range of markers and study populations. Furthermore, the preponderance of positive studies suggests either publication bias or the overwhelming multifactorial nature of frailty. Future investigatory strategies should target biomarkers which are identified through potential mechanistic pathways based on current available information on laboratory and clinical markers. Ethical statement as this is a review article, ethical approval is not necessary. Disclosure of interest The authors declare that they have no competing interest.

Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010

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Please cite this article in press as: Ramakrishnan P, et al. A systematic review of studies comparing potential biochemical biomarkers of frailty with frailty assessments. Eur Geriatr Med (2017), http://dx.doi.org/10.1016/j.eurger.2017.07.010