“I hope it is going to result in licensing, investing, or any other way that moves it forward…If it only exists as a paper in my drawer, what good does it do?” – Eva Redei, PhD, first author.
Media coverage of an article in Translational Psychiatry uniformly passed on the authors’ extravagant claims in a press release from Northwestern University that declared that a simple blood test for depression had been found. That is, until I posted a critique of these claims at my secondary blog. As seen on Twitter, the tide of opinion suddenly shifted and considerable skepticism was expressed.
I am now going to be presenting a thorough critique of the article itself. More importantly, I will be pointing to how, with some existing knowledge and basic tools, many of you can learn to critically examine the credibility of such claims that will inevitably arise in the future. Biomarkers for depression are a hot topic, and John Ioannidis has suggested that means a lot of exaggerated claims about flawed studies are more likely to be the result than real progress.
The article can be downloaded here and the Northwestern University press release here. When I last blogged about this article, I had not seen the 1:58 minute video that is embedded in the press release. I encourage you to view it before my critique and then view it again if you believe that it has any remaining credibility. I do not know where the dividing line is between unsubstantiated claims about scientific research and sheer quackery, but this video tests the boundaries, when evaluated in light of the evidence actually presented in the article.
I am sure that many journalists, medical and mental health professionals, laypersons were intimidated by the mention of “blood transcriptomic biomarkers” in the title of this peer-reviewed article. Surely, the published article had survived evaluation by an editor and reviewers with better, relevant expertise. What is there for an unarmed person to argue about?
Start with the numbers and basic statistics
Skepticism about the study is encouraged by a look at the small numbers of patients involved in the study, which was limited to
- 64 total participants, 32 depressed patients from a clinical trial and 32 controls.
- 5 patients were lost from baseline to follow up.
- 5 more were lost from 18 week blood draws, leaving
- 22 remaining patients –
- 9 classified as in remission, 13 not in remission.
The authors were interested in differences in 20 blood transcriptomic biomarkers in 2 comparisons: the 32 depressed patients versus 32 controls and the 9 patients who remitted at the end of the trial versus 13 who did not. The authors committed themselves to looking for a clinically significant difference or effect size, which, they tell readers, is defined as .45. We can use a program readily available on the web for a power analysis, which indicates the likelihood of obtaining a statistically significant result (p <.05) for any one of these biomarkers, if differences existed between depressed patients and controls or between the patients who improved in the study versus those who did not. Before even putting these numbers into the calculator, we would expect the likelihood is low because of the size of the sample.
We find that there is only a power of 0.426 for finding one of these individual biomarkers significant, even if it really distinguishes between depressed patients and controls and a power of 0.167 for finding a significant difference in the comparison of the patients who improved versus those who did not.
Bottom line is that this is much too small a sample to address the questions in which the authors are interested – less than 50-50 for identifying a biomarker that actually distinguished between depressed patients and controls and less than 1 in 6 in finding a biomarker actually distinguishing those patients who improved versus those who did not. So, even if the authors really have stumbled upon a valid biomarker, they are unlikely to detect it in these samples.
But there are more problems. For instance, it takes a large difference between groups to achieve statistical significance with such small numbers, so any significant result will be quite large. Yet, with such small numbers, statistical significance is unstable: dropping or adding a few or even a single patient or control or reclassifying a patient as improved or not improved will change the results. And notice that there was some loss of patients to follow-up and to determining whether they improved or not. Selective loss to follow-up is a possible explanation of any differences between the patients considered improved and those who are not considered improved. Indeed, near the end of the discussion, the authors note that patients who were retained for a second blood draw differed in gene transcription from those who did not. This should have tempered claims of finding differences in improved versus unimproved patients, but it did not.
So what I am getting at is that this small sample is likely to produce strong results that will not be replicated in other samples. But it gets still worse –
Samples of 32 depressed patients and 32 controls chosen because they match on age, gender, and race – as they were selected in the current study – can still differ on lots of variables. The depressed patients are probably more likely to be smokers and to be neurotic. So the authors made only be isolating blood transcriptomic biomarkers associated with innumerable such variables, not depression.
There can be single, unmeasured variables that are the source of any differences or some combination of multiple variables that do not make much difference by themselves, but do so when they are together present in a sample. So, in such a small sample a few differences affecting a few people can matter greatly. And it does no good to simply do a statistical test between the two groups, because any such test is likely to be underpowered and miss influential differences that are not by themselves so extremely strong that they meet conditions for statistical significance in a small sample.
The authors might be tempted to apply some statistical controls – they actually did in a comparison of the nine versus 13 patients – but that would only compound the problem. Use of statistical controls requires much larger samples, and would likely produce spurious – erroneous – results in such a small sample. Bottom line is that the authors cannot rule out lots of alternative explanations for any differences that they find.
The authors nonetheless claim that 9 of the 20 biomarkers they examined distinguish depressed patients and 3 of these distinguish patients who improve. This is statistically improbable and unlikely to be replicated in subsequent studies.
And then there is the sampling issue. We are going to come back to that later in the blog, but just consider how random or systematic differences can arise between this sample of 32 patients versus 32 controls and what might be obtained with another sampling of the same or a different population. The problem is even more serious when we get down to the 9 versus 13 comparison of patients who completed the trial. A different intervention or a different sample or better follow-up could produce very different results.
So, just looking at the number of available patients and controls, we are not expecting much good science to come out of this study that is pursuing significance levels to define results. I think that many persons familiar with these issues would simply dismissed this paper out of hand after looking at these small numbers.
The authors were aware of the problems in examining 20 biomarkers in such small comparisons. They announced that they would commit themselves to adjusting significance levels for multiple comparisons. With such low ratios of participants in the comparison groups to variables examined, this remains a dubious procedure. Nonetheless, the authors commit themselves to it. However, when it eliminated any differences between the improved and unimproved patients, they simply ignored having done this procedure and went on to discuss results as significant. If you return to the press release and the video, you can see no indication that the authors had applied a procedure that eliminated their ability to claim results as significant. By their own standards, they are crowing about being able to distinguish ahead of time patients who will improve versus those who will not when they did not actually find any biomarkers that did so.
What does the existing literature tell us we should expect?
Our skepticism aroused, we might next want to go to Google Scholar and search for topics such as genetics depression, biomarkers depression, blood test depression, etc. [Hint: when you put a set of terms into the search box and click, then pull down the menu on the far right to get an advanced search.]
I could say this takes 25 minutes because that is how much time I spent, but that would be misleading. I recall a jazz composer who claim to write a song in 25 minutes. When the interviewer expressed skepticism, the composer said “Yeah, 25 minutes and 25 years of experience.” I had the advantage of knowing what I was looking for.
- The largest genome-wide search for a depression gene as of 2012 involved 2431 cases of major depression and 3673 screened controls and then integrated results in a meta-analysis with other studies, altogether totaling 5763 cases and 6901 controls. It achieved essentially null results and the authors concluded
The low heritability of liability for MDD implies an important role for environmental risk factors. Although genotype X environment interaction cannot explain the so-called ‘missing heritability’,52 it can contribute to small effect sizes. Although genotype X environment studies are conceptually attractive, the lessons learned from the most studied genotype X environment hypothesis for MDD (5HTTLPR and stressful life event) are sobering.
Whichever way we look at it, and whether risk variants are common or rare, it seems that the challenge for MDD will be much harder than for the less prevalent more heritable psychiatric disorders. Larger samples are required whether we attempt to identify associated variants with small effect across average backgrounds or attempt to enhance detectable effects sizes by selection of homogeneity of genetic or environmental background. In the long-term, a greater understanding of the etiology of MDD will require large prospective, longitudinal, uniformly and broadly phenotyped and genotyped cohorts that allow the joint dissection of the genetic and environmental factors underlying MDD.
We analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls)…Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD.
So, there is not much encouragement for the present tiny study.
- The very recent article Blood-Based Biomarkers in Depression: Emerging Themes in Clinical Research noted that recent studies suggest that
baseline gene expression may contain too much individual variation to identify biomarkers with a given disease, as was suggested by the studies’ authors.
Furthermore it noted that other recent studies had identified markers that either performed poorly in replication studies or were simply not replicated.
Again, not much encouragement for the tiny present study.
- Then I stumbled upon an invaluable, directly relevant source, a free report from the US Institute of Medicine (IOM), Evolution of Translational OMICS: The Way Forward.
[According to Wiktionary, Omics refers to related measurements or data from such interrelated fields as genomics, proteomics. transcriptomic or other fields.]
The report came about because of numerous concerns expressed by statisticians and bioinformatics scientists concerning the marketing of gene expression-based tests by Duke University. The complaints concerned the lack of an orderly process for validating such tests and the likelihood that these test would not perform as advertised. In response, the IOM convened an expert panel, which noted that many of the studies that became the basis for promoting commercial tests were small, methodological flawed, and relied on statistics that were inappropriate for the size of the samples and the particular research questions.
The committee came up with some strong recommendations for discovering, validating, and evaluating such tests in clinical practice. By these evidence-based standards, the efforts of the authors of the Translational Psychiatry are woefully inadequate and irresponsible in jumping from their modest size study to the claims they are making to the media and possible financial backers, particularly from such a preliminary small study without further replication in an independent sample.
Given that the editor and reviewers of Translational Psychiatry nonetheless accepted this paper for publication, they should be required to read the IOM report. And all of the journalists who passed on ridiculous claims about this article should also read the IOM book.
If we google the same search terms, we come up with lots of press coverage of what work previously claimed as breakthroughs. Almost none of them pan out in replication, despite the initial fanfare. Failures to replicate are much less newsworthy than false discoveries, but once in a while a statement of resignation makes it into the media. For instance,
love biomarkers in all the wrong places
The existing literature suggests that the investigators have a difficult task looking for what is probably a weak signal with a lot of false positives in the context of a lot of noise. Their task would be simpler if they had a well-defined, relatively homogeneous sample of depressed patients. That is so these patients would be relatively consistent in whatever signal they each gave.
With those criteria, the investigators chose was probably the worst possible sample. They obtained their small sample of 32 depressed patients from a clinical trial comparing face-to-face to Internet cognitive behavioral therapy in a sample recruited from primary medical care.
Patients identified as depressed in primary care are a very mixed group. Keep in mind that the diagnostic criteria require that five of nine symptoms be present for at least two weeks. Many depressed patients in primary care have only five or six symptoms, which are mild and ambiguous. For instance, most women experience sleep disturbance weeks after given birth to an infant. But probing them readily reveals that their sleep is being disturbed by the infant. Similarly, one cardinal symptom of depression is the loss of the ability to experience pleasure, but that is confusing item for primary care patients who do not understand that the loss of the ability is supposed to be due to not being able to experience pleasure, rather than not been able to do things that are previously given them pleasure.
And two weeks is not a long time. It is conceivable that symptoms can be maintained that long in a hostile, unsupportive environment but immediately dissipate when the patient is removed from that environment.
Primary care physicians, if they even adhere to diagnostic criteria, are stuck with the challenge of making a diagnosis based on patients having the minimal number of symptoms, with the required symptoms often being very mild and ambiguous in themselves.
So, depression in primary care is inherently noisy in terms of its inability to give a clear signal of a single biomarker or a few. It is likely that if a biomarker ever became available, many patients considered depressed now, would not have the biomarker. And what would we make of patients who had the biomarker but did not report symptoms of depression. Would we overrule them and insist that they were really depressed? Or what about patients who exhibited classic symptoms of depression, but did not have the biomarker. When we tell them they are merely miserable and not depressed?
The bottom line is that depression in primary care can be difficult to diagnose and to do so requires a careful interview or maybe the passage of time. In Europe, many guidelines discourage aggressive treatment of mild to moderate depression, particularly with medication. Rather, the suggestion is to wait a few weeks with vigilant monitoring of symptoms and encouraging the patient to try less intensive interventions, like increased social involvement or behavioral activation. Only with the failure of those interventions to make a difference and the failure of symptoms to resolve the passive time, should a diagnosis and initiation of treatment be considered.
Most researchers agree that rather than looking to primary care, we should look to more severe depression in tertiary care settings, like inpatient or outpatient psychiatry. Then maybe go back and see the extent to which these biomarkers are found in a primary care population.
And then there is the problem by which the investigators defined depression. They did not make a diagnosis with a gold standard, semi structured interview, like the Structured Clinical Interview for DSM Disorders (SCID) administered by trained clinicians. Instead, they relied on a rigid simple interview, the Mini International Neuropsychiatry Interview, more like a questionnaire, that was administered by bachelor-level research assistants. This would hardly pass muster with the Food and Drug Administration (FDA). The investigators had available scores on the interview-administered Hamilton Depression Scale (HAM-D), to measure improvement, but instead relied on the self-report Personal Health Questionnaire (PHQ-9). The reason why they chose this instrument is not clear, but it would again not pass muster with the FDA.
Oh, and finally, the investigators talk about a possible biomarker predicting improvement in psychotherapy. But most of the patients in this study were also receiving antidepressant medication. This means we do not know if the improvement was due to the psychotherapy or the medication, but the general hope for a biomarker is that it can distinguish which patients will respond to one versus the other treatment. The bottom line is that this sample is hopelessly confounded when it comes to predicting response to the psychotherapy.
Why get upset about this study?
I could go on about other difficulties in the study, but I think you can get the picture that this is not a credible study and one that can serve as the basis in search for a blood base, biomarker for depression. It simply absurd to present it as such. But why get upset?
- Publication of such low quality research and high profile attempts to pass it off as strong evidence of damage the credibility of all evidence-based efforts to establish the efficacy of diagnostic tools and treatments. This study adds to the sense that much of what we read in the scientific journals and is echoed in the media is simply exaggerated or outright false.
- Efforts to promote this article are particularly pernicious in suggesting that primary care physicians can make diagnoses of depression without careful interviewing of patients. The physicians do not need to talk to the patients, they can simply draw blood or give out questionnaires.
- Implicit in the promotion of their results has evidence for a blood test of depression is the assumption that depression is a biological phenomenon, strongly influenced by genetic expression, not the environment. Aside from being patently wrong and inconsistent with available evidence, it leads to a reliance on biomedical treatments.
- Wide dissemination of the article and press release’s claims serve to reinforce laypersons and clinicians’ belief in the validity of commercially available blood tests of dubious value. These tests can cost as much as $475 per administration and there is no credible evidence, by IOM standards, that they perform superior to simply talking to patients.
At the present time, there is no strong evidence that antidepressants are on average superior in their effects on typical primary care patients, relative to, say, interpersonal psychotherapy (IPT). IPT assumes that regardless of how depression comes about, patient improvement can come about by understanding and renegotiating significant interpersonal relationships. All of the trash talk of these authors contradicts this evidence-based assumption. Namely, they are suggesting that we may soon be approaching an era where even the mild and moderate depression of primary care can be diagnosed and treated without talking to the patient. I say bollocks and shame on the authors who should know better.