2013/10/01

science 2.0
Predicting The Past Isn’t As Easy As It Sounds
http://www.science20.com/steve_hentges/predicting_past_isn%E2%80%99t_easy_it_sounds-121533

A recent article discussed the question of causation versus statistical association in cross-sectional
epidemiology studies that evaluate the potential for chemicals to cause health effects. In this type ofstudy, health effect and chemical exposure data are collected at the same point in time, which means
there is no way to know, based on the data evaluated, if the exposure preceded the isease. Without
this temporal information, statistical associations between exposure and health effects may bederived, but it is not possible to establish causation.

This issue is of particular current relevance due to the ready availability of databases that contain large
volumes of cross-sectional exposure and health effect data. A good example is the NHANES(National Health and Nutrition Examination Survey) database from CDC. It’s relatively easy to minethe database for statistical associations and, depending on the chosen level of statistical ignificance, it
is almost certain that some statistical associations will be found by chance alone.

What’s not clear though is which, if any, of these statistical associations have any biological meaning(e.g., causation). Nevertheless, given the ready availability of data to analyze, many researchers
have fallen prey to temptation and their resulting statistical associations have frequently been
uncritically, even sensationally, reported in the popular media as evidence of causation.

Image credit: BevNet

The recent article discussed a study on the common chemical bisphenol A (BPA), which is known tohave a short physiological half-life of only a few hours. Studies have shown that BPA levels in urine,
where it is excreted in the form of a metabolite, are highly variable even within a day. This information
alone suggests that measurement of BPA in single urine spot samples is unlikely to be indicative of
long-term exposure. Accordingly, cross-sectional epidemiology studies on BPA that rely on single
urine spot samples are unlikely to provide any information on causation, regardless of what statisticalassociations might be found.

However, there are some circumstances when a cross-sectional study might provide more information
than just statistical associations. For example, if it were known that habitual exposure to a chemical
consistently occurred over long periods of time at a particular time of day (e.g., exposure occurs withdinner every day), and urine spot samples for analysis were consistently collected at the same time ofday (e.g., first-morning voids), it might be possible to predict past exposures based on current
measurements.

Experimental data for specific chemicals is needed to assess whether this is possible, and for BPA a
new study provides just the sort of data needed. In this study, two spot urine samples were collectedfrom each of 80 women over a 1-3 year time period. Almost all of the urine samples were firstmorning voids. The temporal variability of BPA in urine was assessed by calculating an intraclasscorrelation coefficient (ICC), which reflects the relationship between within- and between-personvariance. The ICC can have a value between 0 and 1 with higher values indicating low within-person
variance, which is what would be needed for spot urine sample measurements to have any chance at
predicting past (or future) exposures.

The study found high within-person variability of urinary BPA levels with an ICC value of 0.14, meaning
there is little correlation between BPA levels in spot urine samples collected 1-3 years part. Removing
the few samples that were not first morning voids to improve consistency on timing of sample
collection resulted in little improvement with an ICC value of 0.15. The study found slightly less, but
still high, variability for samples taken <25 months apart (ICC = 0.23) compared to samples taken >25months apart (ICC = 0.06), the latter samples showing almost no correlation at all.

The implications of high variability for epidemiologic studies are quite significant. As stated by theauthors, “investigation of associations between a single urinary bisphenol A measurement and diseaserisk may be challenging in epidemiologic studies.” Given the low ICC values, “challenging” may besomewhat of an understatement.

As appropriately noted by the authors, their results are specific to their study participants and may not
be generalizable to other populations. Although this new study examined variability over a longer timeperiod, other studies that examined shorter time periods found only slightly better results with ICC
values ranging from 0.11 to 0.43. In a CDC study that comprehensively measured BPA levels in urine
over the course of a week, within-day variation was the main contributor to total variation (70%), withbetween-day (21%) and between-person (9%) variability being less significant.

With such high variability demonstrated in multiple studies over shorter time periods, it’s not likely that
other populations will show significantly lower variability over longer time periods than the population
examined in the new study.

Given the high variability in urinary BPA levels over time, the value of cross-sectional epidemiology
studies based on single urine spot samples is certainly questionable. Perhaps the studies could be
useful for hypothesis generation, even though they have no capability to establish causation, but evenhypotheses based on such poor quality data are of questionable value. Since the source of data formany of these studies is the NHANES database, guidance from CDC on the most appropriate use of
the data, of which there are many excellent uses, could be helpful.

But guidance is apparently lacking and even CDC researchers have recently indulged in a crosssectional study on BPA and other short half-life compounds, with only a brief mention in the discussionsection of the issues discussed here

Comments

Helen Barratt (http://www.science20.com/profile/helen_barratt)This is all very interesting Steve, its important that scientists don't publish and claim causal links when
there is no real evidence in the data they are using to support these claims. I had a quick look at theNHANES database which I hadn't heard of before and I will examine it a lot more closely when I havetime. It says there that :As in past health examination surveys, data will be collected on the prevalence of chronic
conditions in the population. Estimates for previously undiagnosed conditions, as well as those
known to and reported by respondents, are produced through the survey. Such information is aparticular strength of the NHANES program.

Risk factors, those aspects of a person’s lifestyle, constitution, heredity, or environment that mayincrease the chances of developing a certain disease or condition, will be examined. Smoking,alcohol consumption, sexual practices, drug use, physical fitness and activity, weight, and dietaryintake will be studied. Data on certain aspects of reproductive health, such as use of oral
contraceptives and breastfeeding practices, will also be collected.
The diseases, medical conditions, and health indicators to be studied include:

Anemia / Cardiovascular disease/ Diabetes / Environmental exposures / Eye diseases

Hearing loss / Infectious diseases / Kidney disease / Nutrition / Obesity / Oral health
Osteoporosis / Physical fitness and physical functioning /Reproductive history and sexual behavior /Respiratory disease (asthma, chronic bronchitis,emphysema) / Sexually transmitted diseases

VisionDo you know whether the NHANES database holds any data about environmental exposure to a toxinknown as β-N-methylamino-l-alanine (BMAA), which is found in blue-green algae and canbioaccumulate in our food chain, particularly fish and crustaceans as well as polluting our watersupply?

Research recently published in the open access journal PLOS ONE and reported here by the Motor
Neurone Disease Association (mnda) claims that BMAA causes proteins inside cells to clump together
and cause cell death. This finding suggests that BMAA may be a potential cause of up to 90% of MND
and Lou Gehrig's ALS and may also be linked to other neurodegenerative diseases like Alzheimer’sand Parkinson's and could lead to the development of new treatments, which is both good and badnews for people suffering from these terrible debilitating neurodegenerative disorders!

My article about researchers identifying a potential blue green algae cause & L-Serine treatment for
Lou Gehrig's ALS, MND, Parkinsons & Alzheimers is at http://www.science20.com/forums/medicine
Helen Barratt | 10/01/13 | 16:44 PM

Reply Comments by Steve Hentges
I've never heard of BMAA but will read your article to learn more now that you've caught my interest. I
did take a quick look at an NHANES summary document(http://www.cdc.gov/nchs/data/nhanes/survey_content_99_12.pdf) to look for BMAA, but didn't find it.
Take a look yourself since you might find other parameters of interest in the long list of things that aremeasured or collected in the survey. I'll bet there is also a way to nominate new items for future
surveys. It might also be worth taking at look at what's measured in the similar Canadian program
called the Canadian Health Measures Survey (http://www.statcan.gc.ca/start-debut-eng.html). The
Canadian program was probably modelled after NHANES but might have some useful differences.

The list of risk factors and diseases that you found on the NHANES website highlights what I think is
one of the very excellent uses of NHANES data, which is to improve our understanding of disease
incidence in the US population and subpopulations. Some accomplishments are listed further down
the NHANES webpage that you visited.

Steve