So what does the test achieve, and what is the evidence behind it?
Claims made for the test
On their website, NPDX describe their test as a 'tool for earlier ASD diagnosis'. Specifically they say:
'It can be difficult to know when to be concerned because kids develop different skills, like walking and talking, at different times. It can be hard to tell if a child is experiencing delayed development that could signal a condition like ASD or is simply developing at a different pace compared to his or her peers...... This is why a biological test, one that’s less susceptible to interpretation, could help doctors diagnose children with ASD at a younger age. The NPDX ASD test was developed for children as young as 18 months old.'They go on to say:
'In our research of autism spectrum disorder (ASD) and metabolism, we found differences in the metabolic profiles of certain small molecules in the blood of children with ASD. The NPDX ASD test measures a number of molecules in the blood called metabolites and compares them to these metabolic profiles.They further state that this is: 'A new approach to thinking about ASD that has been rigorously validated in a large clinical study' and they note that results from their Children’s Autism Metabolome Project (CAMP) study have been 'published in a peer-reviewed, highly-regarded journal, Biological Psychiatry'.
The results of our metabolic test provide the ordering physician with information about the child’s metabolism. In some instances, this information may be used to inform more precise treatment. Preliminary research suggests, for example, that adding or removing certain foods or supplements may be beneficial for some of these children. NeuroPointDX is working on further studies to explore this.
The NPDX ASD test can identify about 30% of children with autism spectrum disorder with an increased risk of an ASD diagnosis. This means that three in 10 kids with autism spectrum disorder could receive an earlier diagnosis, get interventions sooner, and potentially receive more precise treatment suggestions from their doctors, based on information about their own metabolism.'
The test is recommended for a child who:
- Has failed screening for developmental milestones indicating risk for ASD (e.g. M-CHAT, ASQ-3, PEDS, STAT, etc.).
- Has a family history such as a sibling diagnosed with ASD.
- Has an ASD diagnosis for whom additional metabolic information may provide insight into the child’s condition and therapy.
Where are the non-autistic children with developmental delay?
I looked at the published paper from the CAMP study in Biological Psychiatry.
Given the recommendations made by NPDX, I had expected that the study would involve comparison of children with developmental delay to compare metabolomic profiles in those who did and did not subsequently meet diagnostic criteria for ASD.
However, what I found instead was a study that compared metabolomics in 516 children with a certified diagnosis of ASD and 164 typically-developing children. There was a striking difference between the two groups in 'developmental quotient (DQ)', which is an index of overall developmental level. The mean DQ for the ASD group was 62.8 (SD = 17.8), whereas that of the typically developing comparison group was 100.1 (SD = 16.5). This information can be found in Supplementary Materials Table 3.
It is not possible, using this study design, to use metabolomic results to distinguish children with ASD from other cases of developmental delay. To do that, we'd need a comparison sample of non-autistic children with developmental delay.
The CAMP study is registered on ClinicalTrials.gov, where it is described as follows:
'The purpose of this study is to identify a metabolite signature in blood plasma and/or urine using a panel of biomarker metabolites that differentiate children with autism spectrum disorder (ASD) from children with delayed development (DD) and/or typical development (TD), to develop an algorithm that maximizes sensitivity and specificity of the biomarker profile, and to evaluate the algorithm as a diagnostic tool.' (My emphasis)The study is also included on the NIH Project Reporter portfolio, where the description includes the following information:
'Stemina seeks funding to enroll 1500 patients in a well-defined clinical study to develop a biomarker-based diagnostic test capable of classifying ASD relative to other developmental delays at greater than 80% accuracy. In addition, we propose to identify metabolic subtypes present within the ASD spectrum that can be used for personalized treatment. The study will include ASD, DD and TD children between 18 and 48 months of age. Inclusion of DD patients is a novel and important aspect of this proposed study from the perspective of a commercially available diagnostic test.' (My emphasis)So, the authors were aware that it was important to include a group with developmental delay, but they then reported no data on this group. Such children are difficult to recruit, especially for a study involving invasive procedures, and it is not unusual for studies to fail to meet recruitment goals. That is understandable. But it is not understandable that the test should then be described as being useful for diagnosing ASD from within a population with developmental delay, when it has not been validated for that purpose.
Is the test more accurate than behavioural diagnostic tests?
A puzzling aspect of the NPDX claims is a footnote (marked *) on this webpage:
'Our test looks for certain metabolic imbalances that have been identified through our clinical study to be associated with ASD. When we detect one or more imbalance(s), there is an increased risk that the child will receive an ASD diagnosis'
*Compared to the results of the ADOS-2 (Autism Diagnostic Observation Schedule), Second EditionIt's not clear exactly what is meant by this: it sounds as though the claim is that the blood test is more accurate than ADOS-2. That can't be right, though, because in the CAMP study, we are told: 'The Autism Diagnostic Observation Schedule–Second Version (ADOS-2) was performed by research-reliable clinicians to confirm an ASD diagnosis.' So all the ASD children in the study met ADOS-2 criteria. It looks like 'compared to' means 'based on' in this context, but it is then unclear what the 'increased risk' refers to.
How reliable is the test?
A test's validity depends crucially on its reliability: if a blood test gives different results on different occasions, then it cannot be used for diagnosis of a long-term condition. Presumably because of this, the account of the study on ClinicalTrials.gov states: 'A subset of the subjects will be asked to return to the clinic 30-60 days later to obtain a replicate metabolic profile.' Yet no data on this replicate sample is reported in the Biological Psychiatry paper.
I have no expertise in metabolomics, but it seems reasonable to suppose that amines measured in the blood may vary from one occasion to another; indeed in 2014 the authors published a preliminary report on a smaller sample from CAMP, where they specifically noted that, presumably to minimise impact of medication or special diets, blood samples were taken when the child was fasting and prior to morning administration of medication. (34% of the ASD group and 10% of the typically-developing group were on regular medication, and 19% of the ASD group were on gluten and/or casein-free diets).
I contacted the authors to ask for information on this point. They did not provide any data on test-retest reliability beyond stating:
Thirty one CAMP subjects were recruited at random for a test-retest analysis during CAMP. These subjects were all amino acid dysregulation metabotype negative at the initial time point (used in the analysis for the manuscript). The subjects were sampled 30-60 days later for retest analysis. At the second time point the 31 subjects were still metabotype negative. There are plans for additional resampling of a select group of CAMP subjects. These will include metabotype positive individuals.Thus, we do not currently know whether a positive result on the NPDX ASD test is meaningful, in the sense of being a consistent physiological marker in the individual.
Scientific evaluation of the methods used in the Biological Psychiatry paper
The Biological Psychiatry paper describing development of the test is highly complex, involving a wide range of statistical methods. In their previous paper with a smaller sample, the authors described thousands of blood markers and claimed that using machine learning methods, they could identify a subset that discriminated the ASD and typically-developing groups with above chance accuracy. However, they noted this finding needed confirmation in a larger sample.
In the 2018 Biological Psychiatry paper, no significant differences were found for measures of metabolite abundance, failing to replicate the 2014 findings. However, further consideration of the data led the authors to concentrate instead on ratios between metabolites. As they noted: 'Ratios can uncover biological properties not evident with individual metabolites and increase the signal when two metabolites with a negative correlation are evaluated.'
Furthermore, they focused on individuals with extreme values for ratio scores, on the grounds that ASD is a heterogeneous condition, and the interest is in identifying subgroups who may have altered metabolism. The basic logic is illustrated in Figure 1 – the idea is to find a cutoff on the distribution which selects a higher proportion of ASD than typical cases. Because 76% of the sample are ASD cases, we would expect to find 76% of cases in the tail of the distribution. However, by exploring different cutoffs, it can be possible to identify a higher proportion. The proportion of ASD cases above a positive cutoff (or below a negative cutoff) is known as the positive predictive value (PPV), and for some of the ratios examined by the researchers, it was over 90%.
Figure 1: Illustrative distributions of z-scores for 4 of the 31 metabolites in ASD and typical group: this plot shows raw levels for metabolites; blue boxes show the numbers falling above or below a cutoff that is set to maximise group differences. The final analysis focused on ratios between metabolites, rather than raw levels. From Figure S2, Smith et al (2018).
This kind of approach readily lends itself to finding spurious 'positive' results, insofar as one is first inspecting the data and then identifying a cutoff that maximises the difference between two groups. It is noteworthy that the metabolites that were selected for consideration in ratio scores were identified on the basis that they showed negative correlations within a subset of the ASD sample (the 'training set'). Accordingly, PPV values from a 'training set' are likely to be biased and will over-estimate group differences. However, to avoid circularity, one can take cutoffs from the training set, and then see how they perform with a new subset of data that was not used to derive the cutoff – the 'test set'. Provided the test set is predetermined prior to any analysis, and totally separate from the training set, then the results with the test set can be regarded as giving a good indication of how the test would perform in a new sample. This is a standard way of approaching this kind of classification problem.
Usually, the PPV for a test set will be less good than for a training set: this is just a logical consequence of the fact that observed differences between groups will involve random noise as well as true population differences, and these will boost the PPV. In the test set, random effects will be different are so are more likely to hinder rather than help prediction, and so PPV will decline. However, in the Biological Psychiatry paper, the PPVs for the test sets were only marginally different from those from the training sets: for the ratios described in Table 1, the mean PPV was .887 (range .806 - .943) for the training set, and mean .880 (range .757 - .975) for the test set.
I wanted to understand this better, and asked the authors for their analysis scripts, so I could reconstruct what they did. Here is the reply I received from Beth Donley:
We would be happy to have a call to discuss the methodology used to arrive at the findings in our paper. Our scripts and the source code they rely on are proprietary and will not be made public unless and until we publish them in a paper of our own. We think it would be more meaningful to have a call to discuss our approach so that you can ask questions and we can provide answers.My questions were sufficiently technical and complex that this was not going to work, so I provided written questions, to which I received responses. However, although the replies were prompt, they did not really inspire confidence, and, without the scripts I could not check anything.
My question: Is there an explanation for why the PPVs are so similar for training and test datasets? Usually you'd expect a drop in PPV in the test dataset if the function was optimised for the training dataset, just because the training threshold would inevitably be capitalising on chance.But the demographic similarity between a test and training set is not the main issue here. One thing that crucially determines how close the results will be is the reliability of the metabolomic measure. The lower the test-retest reliability of the measure, the more likely that results from a training set will fail to replicate. So it would be helpful if the authors would report the quantitative data that they have on this question.
Response: We observed this phenomenon, as well, and were surprised by the similarity of the training and test confusion matrix performance metric values. We have no way to know why the metrics were similar between sets. Our best guess is that the demographics of the training and test set of subjects had closely matched demographic and study related variables.
If we ignore all the problems, how good is prediction?
Unfortunately, it is virtually impossible to tell how accurate the test would be in a real-life context. First, we would have to make the assumption that a non-autistic group with developmental delay would be comparable to the typically-developing group. If non-autistic children with developmental delay show metabolomic imbalances, then the test's potential for diagnosis of ASD is compromised. Second, we would have to come up with an estimate of how many children who are given the test will actually have ASD: that's very hard to judge, but let us suppose it may be as high as 50%. Then, for the ratios reported in the Biological Psychiatry paper, we can compute that around 50% to 83% of those testing positive would have ASD. Note that the majority of children with and without ASD won't have scores in the tail of the distribution and will not therefore test positive (see Figure 1). On the NPDX website is is claimed that around 30% of children with ASD test positive: That is hard to square this with the account in Biological Psychiatry which reported 'an altered metabolic phenotype' in 16.7% of those with ASD.
Conflict of interest and need for transparency
The published paper gives a comprehensive COI statement as follows:
AMS, MAL, and REB are employees of, JJK and PRW were employees of, and ELRD is an equity owner in Stemina Biomarker Discovery Inc. AMS, JJK, PRW, MAL, ELRD, and REB are inventors on provisional patent application 62/623,153 titled “Amino Acid Analysis and Autism Subsets” filed January 29, 2018. DGA receives research funding from the National Institutes of Health, the Simons Foundation, and Stemina Biomarker Discovery Inc. He is on the scientific advisory boards of Stemina Biomarker Discovery Inc. and Axial Therapeutics.It is generally accepted that just because there is COI, this does not invalidate the work: it simply provides a context in which it can be interpreted. The study reported in Biological Psychiatry represents a huge investment of time and money, with research funds contributed from both public and private sources. In the Xconomy interview, it is stated that the research has cost $8 million to date. This kind of work may only be possible to do with involvement of a biotechnology company which is willing to invest funds in the hope of making discoveries that can be commercialised; this is a similar model to drug development.
Where there is a strong commercial interest in the outcome of research, the best way of counteracting negative impressions is for researchers to be as open and transparent as possible. This was not the case with the NPDX study: as described above, there were substantial changes from the registered protocol on ClinicalTrials.gov, not discussed in the paper. The analysis scripts are not available – this means we have to take on trust details of the methods in an area where the devil is in the detail. As Philip Stark has argued, a paper that is long on results but short on methods is more like an advertisement than a research communication: "Science should be ‘show me’, not ‘trust me’; it should be ‘help me if you can’, not ‘catch me if you can’."
On 27th December, Biological Psychiatry published correspondence on the Smith et al paper by Kristin Sainani and Steven Goodman from Stanford University. They raised some of the points noted above regarding the lack of predictive utility of the blood test in clinical contexts, the lack of a comparison sample with developmental delay, and the conflict of interest issues. In their response, the authors made the point that they had noted these limitations in their published paper.
Sainani, K. L., & Goodman, S. N. (2018). Lack of diagnostic utility of 'amino acid dysregulation metabotypes'. Biological Psychiatry. doi:10.1016/j.biopsych.2018.11.012
Smith, A. M., Donley, E. L. R., Burrier, R. E., King, J. J., & Amaral, D. G. (2018). Reply to: Lack of Diagnostic Utility of “Amino Acid Dysregulation Metabotypes”. Biological Psychiatry. doi: https://doi.org/10.1016/j.biopsych.2018.11.013
Smith, A. M., King, J., J, West, P. R., Ludwig, M. A., Donley, E. L. R., Burrier, R. E., & Amaral, D. G. (2018). Amino acid dysregulation metabotypes: Potential biomarkers for diagnosis and individualized treatment for subtypes of autism spectrum disorder. Biological Psychiatry. doi:https://doi.org/10.1016/j.biopsych.2018.08.016
Stark, P. (2018). Before reproducibiity must come preproducibility. Nature, 557, 613. doi:10.1038/d41586-018-05256-0
West, P. R., Amaral, D. G., Bais, P., Smith, A. M., Egnash, L. A., Ross, M. E., . . . Burrier, R. E. (2014). Metabolomics as a tool for discovery of biomarkers of Autism Spectrum Disorder in the blood plasma of children. PLOS One, 9(11), e112445. doi: https://doi.org/10.1371/journal.pone.0112445