Thousands of strain-specific whole-genome sequences are now available for a wide range of pathogenic bacteria. Using these data, approaches based on machine learning can now be used to predict the results of antimicrobial susceptibility tests from sequence alone. Recent studies have demonstrated the ability to predict minimum inhibitory concentrations with accuracies up to 95 %. Employing these tools to prioritize antibiotic treatment could improve patient outcomes and help to avoid the antibiotic resistance crisis.
by Dr Jonathan M. Monk
Importance of antimicrobial resistance (AMR) prediction
Today over 700 000 people die of antibiotic resistant infections per year [1]. Frighteningly, it has been estimated that this number could rise to 10 million deaths per year if nothing is done to stop the increase and spread of antibiotic resistant bacteria [2]. To help combat this threat it is critical to limit the use of ineffective antibiotics and to prescribe the appropriate antimicrobial therapy to patients as quickly as possible. Although antimicrobial susceptibility testing is now routine in microbiology laboratories, this testing often takes too long to impact clinical diagnosis.
New tools that rapidly predict antibiotic resistance could improve antibiotic stewardship and, when effectively implemented, have led to reductions in levels of resistant bacteria in hospitals [3]. Thus, accurately diagnosing antibiotic resistant bacteria would avoid the evolutionary pressures that accelerate resistance and would aid antibiotic stewardship approaches. This could enable physicians to select the optimal antibiotic regimen to cure a patient, rather than enhancing a given strain’s resistance. Whole-genome sequencing may offer this possibility.
The genomics revolution has made available thousands of strain-specific whole-genome sequences (WGS) for a range of pathogenic bacteria. For example the Pathosystems Resource Integration Center (PATRIC) [the all-bacterial Bioinformatics Resource Center (BRC) funded by the National Institute of Allergy and Infectious Diseases (NIAID)] currently contains over 15 000 Escherichia genomes, more than 14 000 Staphylococcus genomes and nearly 11 000 Mycobacteria genomes [4]. Increasingly, these genomes are coupled with clinical metadata, including minimum inhibitory concentration (MIC) values for various antibiotics.
This large-scale coupling of resistance data with strain-specific genome sequences enables machine learning and other big-data science approaches to study and predict antibiotic resistance. For example, it is now possible to apply case-control studies whereby a group of strains that exhibit a biological phenotype (e.g. antibiotic resistance) is compared to a group of strains that do not. Machine learning techniques can be used to identify biomarkers (e.g. presence/absence of genes or mutations) that are predictive of a given phenotype. These biomarkers can then serve as a basis for diagnostic tests.
Here we discuss recent literature using machine learning approaches to predict antibiotic resistance and highlight considerations required for their application.
Introduction to machine learning approaches for WGS-based prediction of AMR
Setting up a machine learning problem involves breaking data into two groups (Fig. 1a):
(1) The y-array containing genomes or samples (m rows) matched with the phenotype to be predicted. In the case of AMR prediction, a phenotype could be binary e.g. ‘resistant’ versus ‘susceptible’ or the actual experimentally measured MIC. Predicting MICs is often preferable owing to changing breakpoints used to define resistance. For example, a given strain with a MIC of 8 µg/mL gentamicin may have previously been classified as resistant, but new CLSI 2017 guidelines specify that gentamicin resistance requires a MIC above 16 µg/mL. This can lead to inconsistent AMR annotations that can confound binary predictions.
(2) The X-matrix containing the samples (m rows) and their associated features (n columns) that will be used to make a prediction. Features range from those that are completely knowledge-based, such as the presence of genes known to confer antibiotic resistance (e.g. a beta-lactamase) to those that require no previous knowledge such as the presence of short (~10 bp) segments of DNA on the chromosome (Fig. 1b). These features have unique benefits and drawbacks that have been used in several recent studies described below.
Selecting appropriate features for AMR prediction
Knowledge-based features
Knowledge-based features can be obtained by mapping a genome of interest using curated databases of gene products that have already been demonstrated to confer antibiotic resistance. As of February 2019, the Comprehensive Antibiotic Resistance Database (CARD) houses 2553 reference sequences and 1216 SNPs demonstrated to confer resistances for 79 different pathogens [5]. These approaches are akin to laboratory tools that offer PCR-based identification of AMR determinants, such as BioFire. Models trained using previously annotated features often have good accuracies and are easier to interpret because of the accumulated knowledge present in such predictions [6, 7].
However, despite their high accuracy, these tools are limited because they often require many rounds of multiple sequence alignment, which can become computationally expensive at large scale. Also, reliance on known AMR determinants may cause such algorithms to miss newly evolved resistance mechanisms. A useful machine learning approach should be capable of analysing future outbreaks and identifying new mechanisms of resistance, rather than being limited to past knowledge.
Gene- and allele-based features
An approach that balances these two extremes involves assembling features by annotating the genome for known protein coding genes, but keeping the feature types agnostic, for example by including genes with functions ranging from cell replication, to cell wall synthesis to metabolism. This approach has the advantage of not requiring known determinants of antimicrobial resistance but does still require annotated genomes, potentially biasing results by annotation methods.
Recent studies have used this approach to predict antibiotic resistance in E. coli with accuracies above 90 % [8]. Importantly, this approach identified features that outperformed genes established in the literature. Such an approach can go even deeper by breaking the genes down into their constituent alleles to account for potential mutations in each coding sequence. Another study took this approach to examine 1595 strains of M. tuberculosis and identified 33 known AMR-conferring genes and 24 new potentially novel antibiotic resistance conferring genes [9]. Thus, methods that rely on several features extracted from the genome, rather than restricting them to those with previous knowledge, can be used to accurately predict AMR and identify novel mechanisms of resistance making them extensible to mutations and mechanisms of resistance that may emerge in the future.
Kmer feature selection
A contrasting approach that can identify new mechanisms of resistance and requires no a priori knowledge involves breaking up a genome into short (~10 bp) long segments of DNA and using these to create ‘features’ from short segments of DNA on the genome, known as ‘kmers’. All genomes in the collection can be divided into kmers that are then added to the X-matrix where presence of a specific kmer becomes a feature. Thus, this kmer-based approach contrasts with knowledge-based methods to predict AMR that rely on a database of curated genes and mutations previously shown to confer antibiotic resistance.
Studies of A. baumannii, S. aureus, S. pneumoniae, K. pneumoniae and collections of over 5000 Salmonella genomes have demonstrated ability to predict MIC with an average accuracies above 90 % within +/−1 twofold dilution step [10–12]. Unfortunately, this high accuracy and ability to predict new mechanisms of resistance has the trade-off of being difficult to interpret. For example, a model may imply a strong relationship between predicted resistance and segments of the genome without annotated functions or to biological processes.
Building and evaluating a machine learning model
Once the features for a model have been selected it is time to apply a machine learning algorithm to the data. Several such algorithms exist, each with benefits and drawbacks related to accuracy and interpretability [13]. Unfortunately, often the more accurate models are difficult to interpret whereas more intelligible models have worse predictive capabilities. In healthcare applications it is vital for the treating physician to be able to understand, validate and trust a model, and thus relying on easier to interpret methods like a decision tree or simple logistic regressors may be best.
When evaluating a machine learning model it is imperative to question how the model was trained. A major pitfall for machine learning approaches is their tendency to ‘overfit’ datasets. For example, a model trained on data used to make a prediction could simply ‘remember’ that data and use it to correctly predict any point in the same training set. However, if the model is too rigid it may perform poorly on new data. Robust machine learning models avoid such overfitting by splitting the data into non-overlapping sets where ~80 % of the data is used for training and ~20 % of the data is used for tests (Fig. 2a). This splitting process should be random and be performed several times to assess the overall accuracy and sensitivity of a model, thereby limiting overfitting and ensuring that predictions remained generalizable and robust.
Once a model’s ability to predict new data is established it is finally possible to evaluate the model’s predictive performance (Fig. 2b). Thus far, we have described previous studies in terms of correct predictions and accuracies. However, it’s often more important to evaluate cases where a model fails. Requirements for AMR diagnostic devices are strict. Devices typically describe their utility in terms of error rate. Major errors (MEs) occur when susceptible genomes are incorrectly predicted to have resistant MICs. The opposite case, when resistant genomes are incorrectly assigned susceptible MICs, are termed very major errors (VMEs). US Food and Drug Administration (FDA) standards for automated systems recommend a ME rate ≤3 %. A recent study of over 5000 Salmonella genomes used kmers to train a model that demonstrated MIC predictions for 15 antibiotics with ME rates in this range [10]. The FDA standards for VME rates indicate that the lower 95 % confidence limit should be ≤1.5 % and upper limit should be ≤7.5 %. Models for seven of the 15 antibiotics in the same study had acceptable VME rates based on this requirement. Thus, such an approach would make acceptable predictions for diagnostic applications.
Summary and outlook
In summary, options for WGS-based predictions of antimicrobial susceptibility testing are becoming a reality. This brief summary limits the scope to tools and methods to predict antibiotic susceptibility from WGS. However, in the future it may be possible to combine genomic features with information from the patient, like age, gender, comorbidities, etc. Furthermore, rather than predicting only antibiotic susceptibility it would be possible to train an algorithm to predict patient outcome and adjust treatment regimens to improve patient care [14].
Such approaches are sorely needed because despite improvements in antibiotic use, the Centres for Disease Control and Prevention (CDC) estimates that approximately 50 % of antibiotics are still prescribed unnecessarily in the US at a yearly cost of $1.1 billion [15], and the annual impact of resistant infections in the US is estimated to be $20 billion in excess healthcare costs and 8 million additional days patients stay in the hospital [16]. Significant improvements in patient outcome have been observed when reducing the time of treatment with optimal antibiotic therapy [17, 18]. Rapid identification and targeted treatment of pathogenic bacteria using tools assisted by algorithms presented here would enable precision medicine for pathogens that would lower the incidence of antibiotic resistance, improve patient health, and lead to decreased hospital costs.
Figure 1. How to set up a whole-genome sequence (WGS)-based machine learning problem for antimicrobial resistance (AMR) prediction. (a) Samples (m=rows) with sequenced genomes and known phenotypes of interest [‘susceptible’ vs ‘resistant’ phenotypes or minimum inhibitory concentration (MIC) value] are used to train a machine learning model. All values to be predicted are placed into the ‘y’ array. The ‘features’ used to train a model form the columns of the X-matrix. (b) For WGS-based antimicrobial-susceptibility-test prediction possible feature types include: (1) known antibiotic resistance conferring genes or mutations, (2) annotated protein coding genes (independent of known functions) and even (3) the presence of short fragments of DNA sequence on the chromosome known as ‘kmers’. These different feature types have a trade-off between ease of interpretation (easiest for previously identified features) and ability to detect novel AMR determinants (best for short sequence fragments).
Figure 2. Evaluating predictions from a machine learning model. (a) A machine learning model that is ‘overfit’ is inflexible to new data. To ensure a model is robust enough to predict new samples, all models should be cross-validated. This process involves randomly splitting the whole dataset into training (± 80 % of samples) and testing (± 20 %) sets. The sets should be shuffled multiple times to check model accuracy across different samples and features. (b) The results of running the model on testing sets can then be compared for each randomly sampled set (different colored lines). The model’s performance is compared by calculating the area under the curve (AUC) on a plot of the true positive rate vs the false positive rate often called a receiver operating characteristic (ROC) curve. Model accuracy can be calculated from the number of true positive (TP) [model predictions, resistant (R); experimental result, R] and true negative (TN) predictions divided by the total number of predictions. However, it is often more important to gauge how a model fails: for example a false positive ‘major error’ [model prediction, R; experimental result, susceptible (S)] may lead to incorrectly withholding an effective antibiotic. Even worse, false negative ‘very major error’ predictions (model prediction, S; experimental result, R) could lead to prescribing an antibiotic that is ineffective.
References
1. The dangers of hubris on human health. Global Risks – Reports. World Economic Forum 2013 (http://reports.weforum.org/global-risks-2013/risk-case-1/the-dangers-of-hubris-on-human-health/).
2. O’Neill J. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. Rev Antimicrob Resist 2014; 20: 1–16.
3. Carling P, Fung T, Killion A, Terrin N, Barza M. Favorable impact of a multidisciplinary antibiotic management program conducted during 7 years. Infect Control Hosp Epidemiol 2003; 24(9): 699–706.
4. Wattam AR, Abraham D, Dalay O, Disz TL, Driscoll T, Gabbard JL, Gillespie JJ, Gough R, Hix D, Kenyon R, Machi D, Mao C, Nordberg EK, Olson R, Overbeek R, Pusch GD, Shukla M, Schulman J, Stevens RL, Sullivan DE, Vonstein V, Warren A, Will R, Wilson MJ, Yoo HS, Zhang C, Zhang Y, Sobral BW. PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res 2014; 42(Database issue): D581–591.
5. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, Guo P, Tsang KK, Lago BA, Dave BM, Pereira S, Sharma AN, Doshi S, Courtot M, Lo R, Williams LE, Frye JG, Elsayegh T, Sardar D, Westman EL, Pawlowski AC, Johnson TA, Brinkman FS, Wright GD, McArthur AG. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res 2017; 45(D1): D566–573.
6. Jeukens J, Freschi L, Kukavica-Ibrulj I, Emond-Rheault J-G, Tucker NP, Levesque RC. Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa. Ann N Y Acad Sci 2019; 1435(1): 5–17 (First published 2017 Online: https://nyaspubs.onlinelibrary.wiley.com/doi/abs/10.1111/nyas.13358).
7. Bradley P, Gordon NC, Walker TM, Dunn L, Heys S, Huang B, Earle S, Pankhurst LJ, Anson L, de Cesare M, Piazza P, Votintseva AA, Golubchik T, Wilson DJ, Wyllie DH, Diel R, Niemann S, Feuerriegel S, Kohl TA, Ismail N, Omar SV, Smith EG, Buck D, McVean G, et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun 2015; 6: 10063.
8. Her H-L, Wu Y-W. A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains. Bioinformatics 2018; 34(13): i89–i95.
9. Kavvas ES, Catoiu E, Mih N, Yurkovich JT, Seif Y, Dillon N, Heckmann D, Anand A, Yang L, Nizet V, Monk JM, Palsson BO. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat Commun 2018; 9(1): 4306.
10. Nguyen M, Long SW, McDermott PF, Olsen RJ, Olson R, Stevens RL, Tyson GH, Zhao S, Davis JJ. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. J Clin Microbiol 2019; 57(2): pii: e01260-18 (http://dx.doi.org/10.1128/JCM.01260-18).
11. Davis JJ, Boisvert S, Brettin T, Kenyon RW, Mao C, Olson R, Overbeek R, Santerre J, Shukla M, Wattam AR, Will R, Xia F, Stevens R. Antimicrobial resistance prediction in PATRIC and RAST. Sci Rep 2016; 6: 27930.
12. Nguyen M, Brettin T, Long SW, Musser JM, Olsen RJ, Olson R, Shukla M, Stevens RL, Xia F, Yoo H, Davis JJ. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci Rep 2018; 8(1): 421.
13. Deo RC. Machine learning in medicine. Circulation 2015; 132(20): 1920–1930.
14. Kachroo P, Eraso JM, Beres SB, Olsen RJ, Zhu L, Nasser W, Bernard PE, Cantu CC, Saavedra MO, Arredondo MJ, Strope B, Do H, Kumaraswami M, Vuopio J, Gröndahl-Yli-Hannuksela K, Kristinsson KG, Gottfredsson M, Pesonen M, Pensar J, Davenport ER, Clark AG, Corander J, Caugant DA, Gaini S. Integrated analysis of population genomics, transcriptomics and virulence provides novel insights into Streptococcus pyogenes pathogenesis. Nat Genet 2019; 51(3): 548–559 (http://dx.doi.org/10.1038/s41588-018-0343-1).
15. Antibiotic resistance threats in the United States, 2013. US Centers for Disease Control and Prevention 2013 (https://www.cdc.gov/drugresistance/pdf/ar-threats-2013-508.pdf).
16. Fair RJ, Tor Y. Antibiotics and bacterial resistance in the 21st century. Perspect Medicin Chem 2014; 6: 25–64.
17. Kumar A, Roberts D, Wood KE, Light B, Parrillo JE, Sharma S, Suppes R, Feinstein D, Zanotti S, Taiberg L, Gurka D, Kumar A, Cheang M. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med 2006; 34(6): 1589–1596.
18. Palmer HR, Palavecino EL, Johnson JW, Ohl CA, Williamson JC. Clinical and microbiological implications of time-to-positivity of blood cultures in patients with Gram-negative bacilli bacteremia. Eur J Clin Microbiol Infect Dis 2013; 32(7): 955–959.
The author
Jonathan M. Monk PhD
Department of Bioengineering, UC San Diego, San Diego, California, USA
E-mail: jmonk@ucsd.edu
Researchers at the University of Illinois at Chicago and Queensland University of Technology of Australia have developed a device that can isolate individual cancer cells from patient blood samples. The microfluidic device works by separating the various cell types found in blood by their size. The device may one day enable rapid, cheap liquid biopsies to help detect cancer and develop targeted treatment plans.
“This new microfluidics chip lets us separate cancer cells from whole blood or minimally-diluted blood,” said Ian Papautsky, the Richard and Loan Hill Professor of Bioengineering in the UIC College of Engineering and corresponding author on the paper. “While devices for detecting cancer cells circulating in the blood are becoming available, most are relatively expensive and are out of reach of many research labs or hospitals. Our device is cheap, and doesn’t require much specimen preparation or dilution, making it fast and easy to use.”
The ability to successfully isolate cancer cells is a crucial step in enabling liquid biopsy where cancer could be detected through a simple blood draw. This would eliminate the discomfort and cost of tissue biopsies which use needles or surgical procedures as part of cancer diagnosis. Liquid biopsy could also be useful in tracking the efficacy of chemotherapy over the course of time, and for detecting cancer in organs difficult to access through traditional biopsy techniques, including the brain and lungs.
However, isolating circulating tumour cells from the blood is no easy task, since they are present in extremely small quantities. For many cancers, circulating cells are present at levels close to one per 1 billion blood cells. “A 7.5-milliliter tube of blood, which is a typical volume for a blood draw, might have ten cancer cells and 35-40 billion blood cells,” said Papautsky. “So we are really looking for a needle in a haystack.”
Microfluidic technologies present an alternative to traditional methods of cell detection in fluids. These devices either use markers to capture targeted cells as they float by, or they take advantage of the physical properties of targeted cells — mainly size — to separate them from other cells present in fluids.
Papautsky and his colleagues developed a device that uses size to separate tumour cells from blood. “Using size differences to separate cell types within a fluid is much easier than affinity separation which uses ‘sticky’ tags that capture the right cell type as it goes by,” said Papautsky. “Affinity separation also requires a lot of advanced purification work which size separation techniques don’t need.”
The device Papautsky and his colleagues developed capitalizes on the phenomena of inertial migration and shear-induced diffusion to separate cancer cells from blood as it passes through ‘microchannels’ formed in plastic. “We are still investigating the physics behind these phenomena and their interplay in the device, but it separates cells based on tiny differences in size which dictate the cell’s attraction to various locations within a column of liquid as it moves.”
Papautsky and his colleagues ‘spiked’ 5-milliliter samples of healthy blood with 10 small-cell-lung cancer cells and then ran the blood through their device. They were able to recover 93 percent of the cancer cells using the microfluidic device. Previously-developed microfluidics devices designed to separate circulating tumour cells from blood had recovery rates between 50 percent and 80 percent.
When they ran eight samples of blood taken from patients diagnosed with non-small-cell lung cancer, they were able to separate cancer cells from six of the samples using the microfluidic device.
In addition to the high efficiency and reliability of the devices, Papautsky said the fact that little dilution is needed is another plus. “Without having to dilute, the time to run samples is shorter and so is preparation time.” They used whole blood in their experiments as well as blood diluted just three times, which is low compared to other protocols for cell separation using devices based on inertial migration.
University in Illinois at Chicagohttps://tinyurl.com/y6roxnur
Skin autofluorescence predicts incident type 2 diabetes, cardiovascular disease and mortality in the general population
van Waateringe RP, Fokkens BT, Slagter SN, van der Klauw MM, van Vliet-Ostaptchouk JV, et al. Diabetologia 2019; 62(2): 269–280
AIMS/HYPOTHESIS: Earlier studies have shown that skin autofluorescence measured with an AGE reader estimates the accumulation of AGEs in the skin, which increases with ageing and is associated with the metabolic syndrome and type 2 diabetes. In the present study, we examined whether the measurement of skin autofluorescence can predict 4-year risk of incident type 2 diabetes, cardiovascular disease (CVD) and mortality in the general population.
METHODS: For this prospective analysis, we included 72 880 participants of the Dutch Lifelines Cohort Study, who underwent baseline investigations between 2007 and 2013, had validated baseline skin autofluorescence values available and were not known to have diabetes or CVD. Individuals were diagnosed with incident type 2 diabetes by self-report or by a fasting blood glucose ≥7.0 mmol/L or HbA1c ≥48 mmol/mol (≥6.5%) at follow-up. Participants were diagnosed as having incident CVD (myocardial infarction, coronary interventions, cerebrovascular accident, transient ischaemic attack, intermittent claudication or vascular surgery) by self-report. Mortality was ascertained using the Municipal Personal Records Database.
RESULTS: After a median follow-up of 4 years (range 0.5–10 years), 1056 participants (1.4%) had developed type 2 diabetes, 1258 individuals (1.7%) were diagnosed with CVD, while 928 (1.3%) had died. Baseline skin autofluorescence was elevated in participants with incident type 2 diabetes and/or CVD and in those who had died (all P< 0.001), compared with individuals who survived and remained free of the two diseases. Skin autofluorescence predicted the development of type 2 diabetes, CVD and mortality, independent of several traditional risk factors, such as the metabolic syndrome, glucose and HbA1c.
CONCLUSIONS/INTERPRETATION: The non-invasive skin autofluorescence measurement is of clinical value for screening for future risk of type 2 diabetes, CVD and mortality, independent of glycaemic measures and the metabolic syndrome.
A renal genetic risk score (GRS) is associated with kidney dysfunction in people with type 2 diabetes
Zusi C, Trombetta M, Bonetti S, Dauriz M, Boselli ML, et al. Diabetes Res Clin Pract 2018; 144: 137–143
This study aims to investigate whether renal and cardiovascular phenotypes in Italian patients with type 2 diabetes (T2D) could be influenced by a number of disease risk SNPs recently found in genome-wide association studies (GWAS). In 1591 Italian subjects with T2D: (1) 47SNPs associated to kidney function and/or chronic kidney disease (CKD) and 49SNPs associated to cardiovascular disease (CVD) risk were genotyped; (2) urinary albumin/creatinine (A/C) ratio, glomerular filtration rate (eGFR) and lipid profile were assessed; (3) a standard electrocardiogram was performed; (4) two genotype risk scores (GRS) were computed (a renal GRS calculated selecting 39 SNPs associated with intermediate traits of kidney damage and a cardiovascular GRS determined selecting 42 SNPs associated to CVD risk phenotypes). After correction for multiple comparisons, the renal GRS was not associated to A/C ratio (P=0.33), but it was significantly related to decreased eGFR (P=0.005). No association between the cardiovascular GRS and electrocardiogram was detected. Thus, in Italian patients with T2D a renal GRS might predict the decline in glomerular function, suggesting that the clock of diabetes associated CKD starts ticking long before hyperglycemia. Our data support the feasibility of gene-based prediction of complications in people with T2D.
Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study
Huth C, von Toerne C, Schederecker F, de Las Heras Gala T, Herder C, et al. Eur J Epidemiol 2018: doi: 10.1007/s10654-018-0475-8 [Epub ahead of print]
The objective of the present study was to identify proteins that contribute to pathophysiology and allow prediction of incident type 2 diabetes or incident prediabetes. We quantified 14 candidate proteins using targeted mass spectrometry in plasma samples of the prospective, population-based German KORA F4/FF4 study (6.5-year follow-up). 892 participants aged 42–81 years were selected using a case-cohort design, including 123 persons with incident type 2 diabetes and 255 persons with incident WHO-defined prediabetes. Prospective associations between protein levels and diabetes, prediabetes as well as continuous fasting and 2 h glucose, fasting insulin and insulin resistance were investigated using regression models adjusted for established risk factors. The best predictive panel of proteins on top of a non-invasive risk factor model or on top of HbA1c, age and sex was selected. Mannan-binding lectin serine peptidase (MASP) levels were positively associated with both incident type 2 diabetes and prediabetes. Adiponectin was inversely associated with incident type 2 diabetes. MASP, adiponectin, apolipoprotein A-IV, apolipoprotein C-II, C-reactive protein, and glycosylphosphatidylinositol specific phospholipase D1 were associated with individual continuous outcomes. The combination of MASP, apolipoprotein E (apoE) and adiponectin improved diabetes prediction on top of both reference models, while prediabetes prediction was improved by MASP plus CRP on top of the HbA1c model. In conclusion, our mass spectrometric approach revealed a novel association of MASP with incident type 2 diabetes and incident prediabetes. In combination, MASP, adiponectin and apoE improved type 2 diabetes prediction beyond non-invasive risk factors or HbA1c, age and sex.
Association between circulating tumor necrosis factor-related biomarkers and estimated glomerular filtration rate in type 2 diabetes.
Kamei N, Yamashita M, Nishizaki Y, Yanagisawa N, Nojiri S, et al. Sci Rep 2018; 8(1): 15302
Chronic inflammation plays a crucial role in the development/progression of diabetic kidney disease. The involvement of tumor necrosis factor (TNF)-related biomarkers [TNFα, progranulin (PGRN), TNF receptors (TNFR1 and TNFR2)] and uric acid (UA) in renal function decline was investigated in patients with type 2 diabetes (T2D). Serum TNF-related biomarkers and UA levels were measured in 594 Japanese patients with T2D and an eGFR ≥30 mL/min/1.73 m2. Four TNF-related biomarkers and UA were negatively associated with estimated glomerular filtration rate (eGFR). In a logistic multivariate model, each TNF-related biomarker and UA was associated with lower eGFR (eGFR <60 mL/min/1.73 m2) after adjustment for relevant covariates (basic model). Furthermore, UA and TNF-related biomarkers other than PGRN added a significant benefit for the risk factors of lower eGFR when measured together with a basic model (UA, ΔAUC, 0.049, P<0.001; TNFα, ΔAUC, 0.022, P=0.007; TNFR1, ΔAUC, 0.064, P<0.001; TNFR2, ΔAUC, 0.052, P<0.001) in receiver operating characteristic curve analysis. TNFR ligands were associated with lower eGFR, but the associations were not as strong as those with TNFRs or UA in patients with T2D and an eGFR ≥30 mL/min/1.73 m2.
Plasma endostatin predicts kidney outcomes in patients with type 2 diabetes
Chauhan K, Verghese DA, Rao V, Chan L, Parikh CR, et al. Kidney Int 2019; 95(2): 439–446
Novel biomarkers are needed to predict kidney function decline in patients with type 2 diabetes, especially those with preserved glomerular filtration rate (GFR). There are limited data on the association of markers of endothelial dysfunction with longitudinal GFR decline. We used banked specimens from a nested case-control study in the Action to Control Cardiovascular Disease (ACCORD) trial (n=187 cases; 187 controls) and from a diverse contemporary cohort of type 2 diabetic patients from the Mount Sinai BioMe Biobank (n=871) to assess the association of plasma endostatin and kidney outcomes. We measured plasma endostatin at enrolment and examined its association with a composite kidney outcome of sustained 40% decline in estimated GFR or end-stage renal disease. Baseline plasma endostatin levels were higher in participants with the composite outcome. Each log2 increment in plasma endostatin was associated with approximately 2.5-fold higher risk of the kidney outcome (adjusted odds ratio [OR] 2.5; 95% confidence interval [CI] 1.5–4.3 in ACCORD and adjusted hazard ratio [HR] 2.6; 95% CI 1.8-3.8 in BioMe). Participants in the highest versus lowest quartile of plasma endostatin had approximately fourfold higher risk for the kidney outcome (adjusted OR 3.6; 95% CI 1.8-7.3 in ACCORD and adjusted HR 4.4; 95% CI 2.3-8.5 in BioMe). The AUC for the kidney outcome improved from 0.74 to 0.77 in BioMe with the addition of endostatin to a base clinical model. Plasma endostatin was strongly associated with kidney outcomes in type 2 diabetics with preserved eGFR and improved risk discrimination over traditional predictors
Relation of serum and urine renal biomarkers to cardiovascular risk in patients with type 2 diabetes mellitus and recent acute coronary syndromes (from the EXAMINE Trial)
Vaduganathan M, White WB, Charytan DM, Morrow DA, Liu Y, et al. Am J Cardiol 2019; 123(3): 382–391
A deeper understanding of the interplay between the renal axis and cardiovascular (CV) disease is needed in type 2 diabetes mellitus (T2DM). We aimed to explore the prognostic value of a comprehensive panel of renal biomarkers in patients with T2DM at high CV risk. We evaluated the prognostic performance of both serum (Cystatin C) and urine renal biomarkers (neutrophil gelatinase-associated lipocalin, kidney injury molecule-1 protein, and indices of urinary protein excretion) in 5380 patients with T2DM and recent acute coronary syndromes in the EXAMINE trial. Patients requiring dialysis within 14 days were excluded. Single- and multimarker covariate-adjusted Cox proportional hazards models were developed to predict times to events. Primary endpoint was composite nonfatal myocardial infarction, nonfatal stroke, or CV death. Median age was 61 years, 68% were men, and mean baseline estimated glomerular filtration rate (eGFR) was 74 mL/min/1.73 m2. During median follow-up of 18 months, 621 (11.5%) experienced the primary endpoint and 326 (6.1%) patients had died. All renal biomarkers were robustly associated with adverse CV events in step-wise fashion, independent of baseline eGFR. However, in the multimarker prediction model, only Cystatin C (per 1 SD) was associated with the primary endpoint (hazard ratio [HR] 1.28 [1.14 to 1.45]; P≤0.001), death (HR 1.51 [1.30 to 1.74]; P≤0.001), and heart failure hospitalization (HR 1.20 [0.96 to 1.49]; P=0.11). Association between Cystatin C and the primary endpoint was similar in baseline eGFR above and below 60 mL/min/1.73 m2 (Pinteraction >0.05). In conclusion, serum and urine renal biomarkers, when tested alone, independently predict long-term adverse CV events in high-risk patients with T2DM. In an integrative panel of renal biomarkers, only serum Cystatin C remained independently associated with subsequent CV risk. Renal biomarkers informing various aspects of kidney function may further our understanding of the complex interplay between diabetic kidney disease and CV disease.
A plasma circulating miRNAs profile predicts type 2 diabetes mellitus and prediabetes: from the CORDIOPREV study
Jiménez-Lucena R, Camargo A, Alcalá-Diaz JF, Romero-Baldonado C, Luque RM, et al. Exp Mol Med 2018; 50(12): 168
We aimed to explore whether changes in circulating levels of miRNAs according to type 2 diabetes mellitus (T2DM) or prediabetes status could be used as biomarkers to evaluate the risk of developing the disease. The study included 462 patients without T2DM at baseline from the CORDIOPREV trial. After a median follow-up of 60 months, 107 of the subjects developed T2DM, 30 developed prediabetes, 223 maintained prediabetes and 78 remained disease-free. Plasma levels of four miRNAs related to insulin signalling and beta-cell function were measured by RT-PCR. We analysed the relationship between miRNAs levels and insulin signalling and release indexes at baseline and after the follow-up period. The risk of developing disease based on tertiles (T1-T2-T3) of baseline miRNAs levels was evaluated by Cox analysis. Thus, we observed higher miR-150 and miR-30a-5p and lower miR-15a and miR-375 baseline levels in subjects with T2DM than in disease-free subjects. Patients with high miR-150 and miR-30a-5p baseline levels had lower disposition index (P=0.047 and P=0.007, respectively). The higher risk of disease was associated with high levels (T3) of miR-150 and miR-30a-5p (HRT3-T1 = 4.218 and HRT3-T1=2.527, respectively) and low levels (T1) of miR-15a and miR-375 (HRT1-T3 = 3.269 and HRT1-T3=1.604, respectively). In conclusion, our study showed that deregulated plasma levels of miR-150, miR-30a-5p, miR-15a, and miR-375 were observed years before the onset of T2DM and pre-DM and could be used to evaluate the risk of developing the disease, which may improve prediction and prevention among individuals at high risk for T2DM.
Emerging biomarkers, tools, and treatments for diabetic polyneuropathy
Bönhof GJ, Herder C, Strom A, Papanas N, Roden M, Ziegler D. Endocr Rev. 2019; 40(1): 153–192
Diabetic neuropathy, with its major clinical sequels, notably neuropathic pain, foot ulcers, and autonomic dysfunction, is associated with substantial morbidity, increased risk of mortality, and reduced quality of life. Despite its major clinical impact, diabetic neuropathy remains underdiagnosed and undertreated. Moreover, the evidence supporting a benefit for causal treatment is weak at least in patients with type 2 diabetes, and current pharmacotherapy is largely limited to symptomatic treatment options. Thus, a better understanding of the underlying pathophysiology is mandatory for translation into new diagnostic and treatment approaches. Improved knowledge about pathogenic pathways implicated in the development of diabetic neuropathy could lead to novel diagnostic techniques that have the potential of improving the early detection of neuropathy in diabetes and prediabetes to eventually embark on new treatment strategies. In this review, we first provide an overview on the current clinical aspects and illustrate the pathogenetic concepts of (pre)diabetic neuropathy. We then describe the biomarkers emerging from these concepts and novel diagnostic tools and appraise their utility in the early detection and prediction of predominantly distal sensorimotor polyneuropathy. Finally, we discuss the evidence for and limitations of the current and novel therapy options with particular emphasis on lifestyle modification and pathogenesis-derived treatment approaches. Altogether, recent years have brought forth a multitude of emerging biomarkers reflecting different pathogenic pathways such as oxidative stress and inflammation and diagnostic tools for an early detection and prediction of (pre)diabetic neuropathy. Ultimately, these insights should culminate in improving our therapeutic armamentarium against this common and debilitating or even life-threatening condition.
Dementia is one of the leading causes of disability in old age and places a huge burden on society. The growing prevalence of dementia calls for accurate, more accessible biomarkers to facilitate clinical diagnosis and prognosis. Peripheral mediums, such as blood and blood derivates (i.e. plasma and platelets), are currently being investigated for their potential as biomarkers of dementia subtypes. There is a lack of reproducibility in dementia biomarker studies, likely because of unaccounted factors such as age, ethnicity and gender, which is stalling their translation from research to the clinical setting. However, several blood-based biomarkers have been consistently reported from plasma and blood cells, including amyloid and tau protein, clusterin and immunoglobulins, as well as α-synuclein. This review highlights the need for further validation of the current blood-based dementia biomarkers for their routine clinical use.
by Oluwatomi E. S. Akingbade and Prof. Elizabeta B. Mukaetova-Ladinska
Introduction
The increasing incidence of neurodegenerative diseases, such as dementia, legitimizes the search for readily accessible biological markers. At present, there are 50 million people worldwide living with dementia, with 10 million new cases reported each year [1]. Dementia care costs are as high as £26 billion a year [1], with the likelihood of increasing further owing to higher numbers of people living with dementia. The increasing incidence of dementia calls for more efficient diagnosis. Currently, dementia diagnosis relies on extensive clinical assessments, facilitated by invasive (i.e. lumbar puncture) and technical (i.e. MRI) testing, all costly and inefficient in keeping up with the growing number of dementia patients.
The definitive diagnosis of dementia is done neuropathologically, and besides the clinical evidence of dementia, is based on the characteristic hallmarks of plaques and tangles (for Alzheimer’s disease (AD), the most common form of dementia [2]), Lewy bodies (for dementia with Lewy bodies (DLB)), and vascular changes (for vascular dementia). Some of the molecular substrates of the characteristic neuropathological dementia features have been taken forward both in neuroradiological (i.e. β-amyloid radiotracers) and biochemical assessments [i.e. amyloid-β (Aβ42), total tau and phosphorylated tau181 measurements in the cerebrospinal fluid (CSF)] [3]. However, their costs, limited access and invasive approach, as well as involvement in secondary inflammatory processes in dementia [4], are restricting their wider clinical utility. Thus, there is a need for the development of less invasive and more cost-effective peripheral biomarkers to facilitate the clinical diagnosis of dementia.
Unlike CSF, blood and blood derivates (platelets and plasma) are easily accessible in the clinical setting and the potential of using them to corroborate dementia diagnosis will likely lead to earlier, and more accurate dementia diagnoses. Although the blood–central nervous system barrier provides a physiological and physical barrier, changes in peripheral fluids and organs have been identified in people with dementia. Notably, erythrocyte [5] and platelet [6] physiology and function are largely effected in dementia. Proteins in peripheral organs are also being explored: i.e. α-synuclein, p53 protein, tau and amyloid in the skin, kidneys and liver; tau protein in the testes; Aβ1–42 and acetylcholinesterase in serous fluid, as well as amyloid and tau proteins in the gastrointestinal tract. Explorative studies continue to report that peripheral components are influenced in dementia disease. Reoccurring peripheral proteins of interest in the search for dementia biomarkers include α-synuclein, Immunoglobulin G (IgG), Aβ precursor protein (AβPP), clusterin (all found in both platelets and plasma) and myeloperoxidase (MPO, present in plasma only).
Clinical relevance of potential blood-based dementia biomarkers
Peripheral Aβ and AβPP
Aβ plaques in the brain are key pathological hallmarks of AD, and lower Aβ CSF level is a known marker in autopsy-confirmed AD subjects [22]. Aβ is also reported in the periphery, i.e. blood and skin. In a healthy population, plasma Aβ40, Aβ42 and Aβ40/42 ratio levels were significantly higher in older participants than in the younger ones [23]. In the Rotterdam Study Cohort [21], lower levels of plasma Aβ1–28 and Aβ40–42 were linked to higher risks of dementia in older age [7]. In platelets, the AβPP ratio [upper (130kDa) to lower (106–110kDa) immunoreactive bands] has been investigated (Table 1). Thus, there are differences in platelet AβPP ratios of those with cognitive deficits pertaining to dementia (i.e. very mild AD [10]; mild AD [10]; and AD [9–11]) and control patients. However, there are conflicting reports on the differences between the AβPP ratio in patients with varying severity of cognitive deficit with some studies [9,10]. Additionally, some studies have reported that increased AβPP levels in platelets correlated with higher Mini-Mental State Examination (MMSE) [11, 12] and Cambridge Cognition Examination (CAMCOG) scores [11]. Whether or not peripheral measurements of Aβ can be used as a pre-symptomatic marker of dementia (and/or the associated cognitive decline) remains uncertain. However, the studies mentioned in this review provide a consistent foundation for future studies to standardize measurement techniques that can be used in the clinical setting.
Peripheral α-synuclein
Studies have consistently reported the potential of α-synuclein as a biomarker of dementia – particularly in DLB as α-synuclein is reported to be a key constituent of Lewy bodies [24]. About 95 % of the α-synuclein in blood is predominantly found in erythrocytes, with the levels in plasma being extremely low. It is only recently that an assay was developed that is sensitive enough to detect the low levels of α-synuclein in plasma – reported in the range of 2.1–19.4 µg/L [25]. However, platelet α-synuclein levels in dementia have been reported to be similar among control and AD subjects [12] (Table 1). The utility of α-synuclein as a dementia biomarker is somewhat questionable, largely owing to its unknown physiological function in the human brain, as well as its involvement in Lewy body formation, which can be present in both normal aging as well as in a number of synucleinopathies, including Parkinson’s disease, multiple system atrophy, DLB, amyotrophic lateral sclerosis, etc, [26].
Blood clusterin
Increased plasma clusterin levels were reported to be indicative of both brain atrophy in AD patients [27] and an increased risk of dementia in older people [13]. Indeed. elevated levels of plasma clusterin have been reported as a risk factor for dementia [13, 28]. One study identified that lower MMSE cognitive scores in an AD population were associated with higher levels of plasma clusterin, whereas AD patients with higher cognitive scores had lower plasma clusterin levels [15]. Furthermore, longitudinal assessments identified that increased plasma clusterin concentrations are related to cognitive decline in mild cognitive impairment [28]. Interestingly, platelet levels of clusterin appear to be similar in both control and AD subjects [12, 16], but the ratio of platelet and plasma clusterin is positively correlated to specific neuropsychiatric inventory sub-categories, in particular agitation, apathy, motor aberrant behaviour and irritability seen in AD subjects [12] (Table 1).
Peripheral immunoglobulins
Increased IgG levels in plasma of AD subjects has been reported [17] with no link to disease progression. In an independent study, plasma IgG levels remained unaltered but increased in the platelet samples of the same AD subjects when compared to IgG levels in their control counterparts [12]. Inconsistent results found in IgG in plasma may be due to experimental constraints and, therefore, the potential of IgGs to form part of dementia diagnosis should not be ruled out. A recent study has reported that electrochemical techniques can be used to detect immunoglobulin in the plasma in the pg/mL range [29] – such sensitivity will likely aid in more consistent findings in plasma IgG studies in dementia.
Peripheral tau protein
CSF tau (and its ratio to Aβ) has been extensively investigated in relation to dementia. A recent literature review has addressed the limited clinical value of CSF tau biomarker studies [30], attesting to the need to address the uncertainty behind CSF tau as a suitable peripheral biomarker for dementia. More recently studies have focused on the presence of tau in the periphery. In plasma, high levels of tau were weakly associated with AD and were longitudinally associated with increased brain atrophy and poor cognition [18]. Platelet tau protein showed a more complex relationship than the reported plasma tau protein levels; studies have also reported a higher ratio between high to low molecular weight tau ratio in AD patients when compared to controls [19, 31] – with acknowledgement of no significant differences found between low or high molecular weight tau protein levels in the platelets of control and AD patients [19] (Table 1). Another study, reported a negative correlation between total tau and phosphorylated tau in the platelets of AD participants [20]. All in all, investigation into peripheral tau as a biomarker of dementia (and even specific dementia subtypes) is still in the early stages and requires further investigation before it can be considered for use in clinical diagnosis.
Summary and conclusions
Historically, the search for peripheral, blood-based dementia biomarkers has focused largely on the protein profiles of plasma and serum in dementia patients. This review, based on 13 dementia biomarker studies, has shown that proteins in the periphery are influenced by neurodegeneration in dementia. Namely, increased concentrations of Aβ40, Aβ42 and clusterin in plasma were all shown to be indicative of an increased risk of developing dementia in the elderly. Most recently, increased levels of plasma neurofilament light chain were reported to be closely related to amyloid processing in both mild cognitive impairment and AD, and to correlate with poor cognitive performance and AD-related brain atrophy and hypometabolism [32]. Changes in proteins in platelets were shown to coincide with cognitive decline in dementia: lower levels of AβPP and higher levels of clusterin were present in those with poorer performance in cognitive tests. Interestingly, increased plasma levels of tau protein were associated with brain atrophy while total and phosphorylated tau levels in platelets were negatively correlated. These findings provide encouraging evidence for the measurable presence of blood-based proteins that are closely linked to AD hallmarks that have the potential to be used in routine clinical setting not only for diagnosis, but also for severity and progression of the dementia process. The reproducibility and causes of possible heterogeneity, i.e. age, ethnicity, co-morbidity and genetics, that may influence protein expression at the periphery will need to be explored further before these biomarkers can be used routinely in the clinic, and their accuracy for distinct dementia subtypes will also need to be determined.
References
1. Dementia. World Health Organization 2017 (http://www.who.int/en/news-room/fact-sheets/detail/dementia).
2. Types of dementia. Alzheimer’s Research UK 2018 (https://www.alzheimersresearchuk.org/about-dementia/types-of-dementia/).
3. Andreasen N, Vanmechelen E, Vanderstichele H, Davidsson P, Blennow K. Cerebrospinal fluid levels of total-tau, phospho-tau and A beta 42 predicts development of Alzheimer’s disease in patients with mild cognitive impairment. Acta Neurol Scand Suppl 2003; 179: 47–51.
4. Veitinger M, Varga B, Guterres SB, Zellner M. Platelets, a reliable source for peripheral Alzheimer’s disease biomarkers? Acta Neuropathol Commun 2014; 2: 65.
5. Stevenson A, Lopez D, Khoo P, Kalaria RN, Mukaetova-Ladinska EB. Exploring erythrocytes as blood biomarkers for Alzheimer’s disease. J Alzheimers Dis 2017; 60(3): 845–857.
6. Akingbade OES, Gibson C, Kalaria RN, Mukaetova-Ladinska EB. Platelets: peripheral biomarkers of dementia? J Alzheimers Dis 2018; 63(4): 1235–1259.
7. Hilal S, Wolters FJ, Verbeek MM, Vanderstichele H, Kamran Ikram M, Stoops E, Ikram MA, Vernooij MW. Plasma amyloid-β levels, cerebral atrophy and risk of dementia: a population-based study. Alzheimers Res Ther 2018; 10: 63 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026500/).
8. Tang K, Hynan LS, Baskin F, Rosenberg RN. Platelet amyloid precursor protein processing: a bio-marker for Alzheimer’s disease. J Neurol Sci 2006; 240(1–2): 53–58.
9. Borroni B, Colciaghi F, Corsini P, Akkawi N, Rozzini L, Del Zotto E, et al. Early stages of probable Alzheimer disease are associated with changes in platelet amyloid precursor protein forms. Neurol Sci 2002; 23(5): 207–210.
10. Padovani A, Borroni B, Colciaghi F, Pettenati C, Cottini E, Agosti C, Talarico G, Cattabeni F, Lenzi GL, et al. Abnormalities in the pattern of platelet amyloid precursor protein forms in patients with mild cognitive impairment and Alzheimer disease. Arch Neurol 2002 Jan; 59(1): 71–75.
11. Zainaghi IA, Forlenza O V, Gattaz WF. Abnormal APP processing in platelets of patients with Alzheimer’s disease: correlations with membrane fluidity and cognitive decline. Psychopharmacology (Berl) 2007; 192(4): 547–553.
12. Mukaetova-Ladinska EB, Abdel-All Z, Dodds S, Andrade J, Alves da Silva J, Kalaria RN, O’Brien JT. Platelet immunoglobulin and amyloid precursor protein as potential peripheral biomarkers for Alzheimer’s disease: findings from a pilot study. Age Ageing 2012; 41(3): 408–412.
13. Weinstein G, Beiser AS, Preis SR, Courchesne P, Chouraki V, Levy D, Seshadri S. Plasma clusterin levels and risk of dementia, Alzheimer’s disease, and stroke. Alzheimer’s Dement 2016; 3: 103–109.
14. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev Med 1975; 4(4): 518–525.
15. Hsu J-L, Lee W-J, Liao Y-C, Wang S-J, Fuh J-L. The clinical significance of plasma clusterin and Abeta in the longitudinal follow-up of patients with Alzheimer’s disease. Alzheimers Res Ther 2017 Nov; 9(1): 91.
16. Mukaetova-Ladinska EB, Abdel-All Z, Andrade J, Alves da Silva J, O’Brien JT, Kalaria RN. Plasma and platelet clusterin ratio is altered in Alzheimer’s disease patients with distinct neuropsychiatric symptoms: findings from a pilot study. Int J Geriatr Psychiatry 2015; 30(4): 368–375.
17. Bosman GJ, Van der Linden PA, Bartholomeus IG, De Man AJ, De Grip WJ, Van Kalmthout PJ. Erythrocyte aging in the demented elderly: a fluctuating process? Mech Ageing Dev 1998; 100(1): 53–58.
18. Mattsson N, Zetterberg H, Janelidze S, Insel PS, Andreasson U, Stomrud E, Palmqvist S2, Baker D2, Tan Hehir CA, et al. Plasma tau in Alzheimer disease. Neurology 2016; 87(17): 1827–1835.
19. Slachevsky A, Guzmán-Martínez L, Delgado C, Reyes P, Farías GA, Muñoz-Neira C, Bravo E, Farías M, Flores P, et al. Tau platelets correlate with regional brain atrophy in patients with Alzheimer’s disease. J Alzheimers Dis 2017; 55(4): 1595–1603.
20. Mukaetova-Ladinska EB, Abdell-All Z, Andrade J, da Silva JA, Boksha I, Burbaeva G, Kalaria RN, O’Brien JT. Platelet tau protein as a potential peripheral biomarker in Alzheimer’s disease: an explorative study. Curr Alzheimer Res 2018; 15(9): 800–808.
21. Hofman A, Brusselle GGO, Darwish Murad S, van Duijn CM, Franco OH, Goedegebure A, Ikram MA, Klaver CC, Nijsten TE, et al. The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol 2015; 30(8): 661–708.
22. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 2009; 65(4): 403–413.
23. Kiko T, Nakagawa K, Satoh A, Tsuduki T, Furukawa K, Arai H, Miyazawa T. Amyloid beta levels in human red blood cells. PLoS One 2012; 7(11): e49620.
24. Koehler NKU, Stransky E, Shing M, Gaertner S, Meyer M, Schreitmuller B, Leyhe T, Laske C, Maetzler W, et al. Altered serum IgG levels to alpha-synuclein in dementia with Lewy bodies and Alzheimer’s disease. PLoS One 2013; 8(5): e64649.
25. Fjorback AW, Varming K, Jensen PH. Determination of alpha-synuclein concentration in human plasma using ELISA. Scand J Clin Lab Invest 2007; 67(4): 431–435.
26. Spillantini MG, Schmidt ML, Lee VM-Y, Trojanowski JQ, Jakes R, Goedert M. α-Synuclein in Lewy bodies. Nature 1997; 388: 839 (http://dx.doi.org/10.1038/42166).
27. Thambisetty M, Simmons A, Velayudhan L, Hye A, Campbell J, Zhang Y, Wahlund LO, Westman E, Kinsey A, et al. Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch Gen Psychiatry 2010 Jul; 67(7): 739–748.
28. Jongbloed W, van Dijk KD, Mulder SD, van de Berg WDJ, Blankenstein MA, van der Flier W, Veerhuis R. Clusterin levels in plasma predict cognitive decline and progression to Alzheimer’s disease. J Alzheimers Dis 2015; 46(4): 1103–1110.
29. Garyfallou G-Z, Ketebu O, Sahin S, Mukaetova-Ladinska EB, Catt M, Yu EH. Electrochemical detection of plasma immunoglobulin as a biomarker for Alzheimer’s disease. Sensors 2017; 17(11): doi: 10.3390/s17112464.
30. Ritchie C, Smailagic N, Noel-Storr AH, Ukoumunne O, Ladds EC, Martin S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane database Syst Rev 2017; 3: CD010803.
31. Neumann K, Farías G, Slachevsky A, Perez P, Maccioni RB. Human platelets tau: a potential peripheral marker for Alzheimer’s disease. J Alzheimers Dis 2011; 25(1): 103–109.
32. Mattsson N, Andreasson U, Zetterberg H, Blennow K. Association of plasma neurofilament light with neurodegeneration in Patients With Alzheimer disease. JAMA Neurol 2017; 74(5): 557–566.
The authors
Oluwatomi E. S. Akingbade1,2 and Elizabeta B. Mukaetova-Ladinska*2,3 MD, PhD, MRCPsych
1School of Life Sciences, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
2Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
3Evington Centre, Leicester General Hospital, Leicester, UK
*Corresponding author
E-mail: eml12@le.ac.uk
Tick-borne encephalitis (TBE) is caused by the TBE virus (TBEV) and is primarily transmitted to humans through a bite from an infected tick. TBEV (a flavivirus) has three genetically and antigenically closely related subtypes: the European, Siberian and Far-Eastern. TBE is endemic in mainland Europe, Scandinavia and Asia, and has recently been found for the first time in a small number of ticks in two parts of the UK: in Thetford Forest, Norfolk, and in the Hampshire–Dorset border area (Holding et al. Tick-borne encephalitis virus, United Kingdom. Emerg Infect Dis. 2020; 26(1): doi: 10.3201/ eid2601.191085). The epidemiology differs substantially between low- and high-risk areas. In different endemic areas, the risk of infection for humans after a single tick bite varies between 1 : 200 and 1 :1000. It is estimated that one third of infections result in clinical symp-toms, which typically follow a biphasic course. This starts with non-specific flu-like symptoms, followed by an asymptomatic interval. The second phase then develops where the central nervous system is affected, ranging from mild meningitis to severe encephalitis with or without myelitis and spinal paralysis. The European subtype is associated with more mild disease, with mortality rates from 0.5 to 2 % and severe neurological conse-quences in up to 10% of patients. There is no known treatment but vaccination is very effective at disease prevention. Because of the vague clinical symptoms, TBE diagnosis has to be confirmed by clinical lab detection of specific anti-TBEV antibodies in blood or cerebrospinal fluid by enzyme-linked immunosorbent assay, immunofluorescence assay or hemagglutination inhibition. TBE antibodies appear 0–6 days after onset and are usually detected when neurological symptoms are present. Care is needed in interpretation of results; specific IgM antibodies can persist for up to 10 months in vaccinees or individuals who acquired the infection naturally; IgG antibody cross-reaction is possibly observed with other flaviviruses (such as louping ill virus, which is already present in the UK). Detection of genetic material by PCR methods could be valuable for an early differential diagnosis of TBE but depends on timely analysis in the short window of viremia. The risk of acquiring TBE in the UK is very low – a greater to risk to UK nationals is holidaying in high-risk areas, such as Austria, Germany, Switzerland and central Europe. However, now we know it is here and we know we can test for it, we need awareness in the public and GPs to make the association between the tick bite and the vague symptoms and the test to be requested – which as we know from Lyme disease is a challenge.
February | March 2025
Beukenlaan 137
5616 VD Eindhoven
The Netherlands
+31 85064 55 82
info@clinlabint.com
PanGlobal Media is not responsible for any error or omission that might occur in the electronic display of product or company data.