Saturday morning, feeling guilty about making a second (or was it third?) cup of coffee, I was mucking around on PubMed to see if I could see if we really know if it benefits health. You can’t read the news without hearing someone extolling the supposed health virtues of consuming luxury goods like coffee, tea, dark chocolate, red wine… Except not so much red wine anymore, since it seems the medical community finally agreed ethanol causes seven types of cancer, and yes, a little poison is still bad for you – it just looked possibly beneficial in moderation for a long time due to confounding (see also: luxury goods).
So I had in mind a recent Guardian piece on possible health benefits of coffee-drinking (1). (But I can only include two links in this post as a new user, so I’ll number the missing ones in the text and post them in a comment later if people care and that’s allowed.) It’s on this paper: ACP Journals. The authors claim their analysis is novel because it distinguishes unsweetened, sweetened naturally, and artificially sweetened coffee consumption. They find a U-shaped association between coffee-drinking without artificial sweetener and lower risk of death; unsurprisingly, black coffee beats sugared coffee for health benefit.
This raises the question of how the other lit on coffee and health cuts up the data, and if we can see these sweetener sub-stories plausibly hiding in them. Here’s an analysis that uses continuous and then categorical analyses: Association between coffee drinking and telomere length in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial - PMC . It seems fine to call the continuous results null (OR = 1.01, 95% CI = 0.99-1.03) — which could be consistent with the sweetener substories, if a lot of people in the sample used artificial sweeteners. Those do seem to get double-digit use in the relevant (U.S.) population (2). So it seems plausible that sweetener substories may have made some false null results.
But then why also run the categorical analyses here? And what to make of the substantial possible effects they show? As in, OR <3 cups/day vs. none = 1.37, 95% CI = 0.71-2.65; OR ≥3 cups/day vs. none = 1.47, 95% CI = 0.81-2.66… and “in the largest of the four contributing studies, moderate (<3 cups/day) and heavy coffee drinkers (≥3 cups/day) were 2.10 times (95% CI = 1.25, 3.54) and 1.93 times as likely (95% CI = 1.17, 3.18) as nondrinkers to have above-median telomere length, respectively.”
In light of those findings, the abstract’s dismissal of the categorical results as null (“no evidence that coffee drinking is associated with telomere length”) seems wrong. But should we be asking why categorical analyses were done after continuous in the first place?
There’s apparently a possible mechanism for anti-aging effects of coffee/caffeine (3). But, other research says their anti-aging effects work in opposite directions, with coffee lengthening telomeres and caffeine shortening them (4). This is apparently an area of contention, as other studies do suggest anti-aging effects of caffeine itself (5).
It seems we have enough evidence about possible mechanisms to doubt the null that coffee doesn’t affect aging or otherwise have longevity-relevant benefits— but not enough to know which way a caffeine effect should then go. So what do we make of the null continuous findings, the choice to also analyze categorically, and the resultant substantial possible effects? Does this point to broader possible consistency in the literature, where we need to analyze smaller subgroups (e.g., black/sugar/artificially sweetened, # of cups…) to see real effects, and the sweetener sub-story is part of that striding toward truth with more precision? Or does this look more like a possible sparse cell count issue, where the more you slice up the data into different analytical categories, the less power as well as precision you have – possibly accounting for the divergent results within and across analyses? And, what else is there to learn here about continuous versus categorical variable analysis choices? I’m looking at F2Harrell’s post on “Categorizing Continuous Variables,” thinking, lots of these points seem to apply here… PubMed returns 32 results relevant to #15 there, for coffee and restricted cubic spline. The first ten of those analyses suggest possible benefit.
PubMed returns null results for searches like “coffee experiment anti-aging” and “coffee experiment telomeres.” So I guess not enough people are committed enough to find out what the telomere/other effects of coffee consumption over six months are, to go on or off the stuff at random. It’s almost like this stuff is addictive…