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Biases Inherent in Studies of Coffee Consumption in Early Pregnancy and the Risks of Subsequent Events

Boston Children’s Hospital and Harvard Medical School, 1731 Beacon Street, Brookline, MA 02445, USA
Nutrients 2018, 10(9), 1152;
Submission received: 24 July 2018 / Revised: 18 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
(This article belongs to the Special Issue The Impact of Caffeine and Coffee on Human Health)


Consumption of coffee by women early in their pregnancy has been viewed as potentially increasing the risk of miscarriage, low birth weight, and childhood leukemias. Many of these reports of epidemiologic studies have not acknowledged the potential biases inherent in studying the relationship between early-pregnancy-coffee consumption and subsequent events. I discuss five of these biases, recall bias, misclassification, residual confounding, reverse causation, and publication bias. Each might account for claims that attribute adversities to early-pregnancy-coffee consumption. To what extent these biases can be avoided remains to be determined. As a minimum, these biases need to be acknowledged wherever they might account for what is reported.

1. Introduction

Maternal consumption of coffee during early pregnancy has been viewed as increasing the risk of miscarriage [1,2,3,4], fetal growth restriction [2,5,6,7,8,9,10,11], and childhood leukemias [12,13,14,15,16,17,18,19,20]. Unfortunately, many of the epidemiologic studies have not acknowledged the potential biases that appear to have influenced these perceptions of risk. The list of potential biases is long [21].
In this essay, I review five of these biases, namely recall bias, misclassification, residual confounding, reverse causation, and publication bias. Each of these biases might account for some of what has been reported. Unfortunately, eliminating these biases can sometimes be extraordinarily difficult, if not impossible. Indeed, a Cochrane Review concluded, “There is insufficient evidence to confirm or refute the effectiveness of caffeine avoidance on birthweight or other pregnancy outcomes. There is a need to conduct high-quality, double-blinded random clinical trials (RCTs) to determine whether caffeine has any effect on pregnancy outcome.” [22]. In essence, observational studies are probably not able to overcome some of the biases. I know of only two clinical trials and they have shown no adverse effect of caffeine consumption on the risk of low birth weight [23], or miscarriage [24].

2. Bias 1: Recall/Respondent Bias

Recall or respondent bias occurs when the person interviewed does not fully report what is asked or tends to remember the past differently than others. Perhaps the most common form of recall bias occurs when respondents want to present themselves in an idealized light. For example, based on a review of 67 studies that examined the relationship between self-reported smoking and smoking confirmed by cotinine (a metabolite of nicotine) measurement in saliva or urine, the authors concluded, “Overall, the data show trends of underestimation when smoking prevalence is based on self-report.” [25]. Indeed, approximately 20% of pregnant women who report that they are not smokers have smoker-level cotinine concentrations in blood or saliva [26,27]. A review of 34 papers concluded that obese adults tend to significantly under-report their food intake [28]. These reports document that people do not always report the truth.
One of the explanations offered for much recall/respondent bias is social desirability [29]. As applied to answering questionnaires, social desirability is seen as having two components [30]. One, identified as ‘impression management,’ is the conscious tendency to deceive others, while the other, labeled, ‘self-deception,’ is the unconscious tendency to believe one’s own positive self-reports. Either way, those who try to get accurate information are thwarted by social desirability [31]. whether they want to study hand washing [32], or tobacco consumption [25,33].
Another form of recall bias occurs when some respondents try harder than others to remember the past. For example, when asked to remember exposures during early pregnancy, the mothers of children who developed leukemia are more likely to report higher coffee consumption than the mothers of children selected from the same community, or the mothers of children hospitalized with acute orthopedic trauma [12,13,14,15,16,17,18,19,20,34,35,36]. How well do people remember what they drank years before? The time between the consumption and the query is not the only influence on the accuracy of the information provided.
Compared to the mothers of healthy newborns, mothers of children with a major congenital malformation diagnosed soon after birth tend to recall more exposures or characteristics during the index pregnancy [37]. This led to the inference that mothers of malformed babies are more likely to try hard to account for what happened than mothers of children who do not have obvious malformations. Preferential recall was also raised by the authors of one study when fathers of children who had leukemia reported levels of cigarette smoking similar to those reported by fathers of ontrols, but mothers of children with leukemia cases reported higher exposure levels to passive smoking than did the mothers of controls [38].
The authors of a meta-analysis of studies that evaluated the relationship between maternal coffee consumption and the risk of childhood leukemia acknowledged the possibility that mothers of children who had leukemia might recall exposures during the index pregnancy differently than community controls (“the possibility of a recall bias could not be precluded”) [39]. On the other hand, another meta-analysis “noted the positive association between coffee consumption and childhood ALL and childhood AML among studies using interviewing techniques, but not among studies using self-administrated questionnaire” [40]. The differential recall implies bias somewhere along the information-gathering process.
In light of these phenomena, strategies to minimize recall bias take several forms. “Cohort studies are generally regarded as providing stronger evidence than case-control studies for causality because they satisfy the temporality criterion that the measurement of exposure precede the ascertainment of the outcome.” [41]. Not surprisingly then, that some tobacco-related exposures (including coffee consumption) are not associated with tobacco-related malignancies in cohort studies (dependent on exposure data collected before recognition of the disorder) [42,43,44], but are reported as associated in case-control studies (dependent on exposure data collected after recognition of the disorder) [45,46].
Because of the potential recall bias even when the exposure was recent, some studies of the relationship between caffeine consumption and miscarriage assessed consumption prior to pregnancy [47,48,49,50]. “Overall, while most of these studies were small, the majority showed that pre-pregnancy consumption of caffeine was not associated with increased risk of spontaneous abortion.” [51].
One way to minimize recall bias that might have contributed to the association between maternal gestational coffee consumption and childhood leukemias would be to choose controls who also have a potentially fatal illness that might have antenatal origins. This strategy of selecting controls who have another disorder that prompts the mother to search her memory especially thoroughly [52], has yet to be applied to the study of childhood leukemia. It would be reasonable to do so if the malignancies of controls each had a relatively unique risk profile.

3. Bias 2: Misclassification

The most obvious misclassification that has the potential to distort our perception of truth about relationships between coffee drinking and any disorder is inappropriately quantifying exposure [53]. What is a cup of coffee? 5 ounces (150 mL)? 8 ounces (240 mL)? Is a mug 8 ounces (240 mL)? 10 ounces (300 mL)? 12 ounces (360 mL)? Similar concerns apply to the ‘strength of the brew’, as well as to additives (e.g., sugar, non-nutritive sweeteners, milk, cream).
Misclassification bias is potentially high in studies that assess the effects of caffeine as the exposure of interest. Almost invariably, authors make assumptions based on reports of caffeine content of coffee, tea, other beverages and foods [54,55,56], and about attributing to a population, the caffeine content as estimated by self-report [57,58].

4. Bias 3: Residual Confounding

Confounding defines the distortion of our perception of the relationship between an exposure (coffee consumption) and a disorder (e.g., childhood leukemia, miscarriage).
This distortion occurs when a variable that is a potential confounder is not considered in the analysis. A potential confounder has to be associated with the disorder and the exposure, but must not be on the causal pathway between the exposure and the disorder [59].
Tobacco smoke induces cytochrome P450 1A2 (CYP1A2), the main enzyme involved in caffeine metabolism, thereby increasing the rate of caffeine metabolism, and shortening the half-life of caffeine [60,61,62]. One consequence is that the duration of desired behavioral effects of caffeine is shortened, prompting smokers to consume more coffee than non-smokers [63]. Among Norwegian pregnant women, the average daily caffeine consumption varied with smoking. For example, never-smokers consumed 54 mg of caffeine daily, while occasional smokers consumed 109 mg daily, and daily smokers consumed on average 143 mg each day [8]. Therefore, tobacco is a potential confounder of the relationship between a mother’s coffee consumption and her child’s risk of childhood leukemia. This can be minimized to some extent by “adjusting” for tobacco exposure.
Residual confounding occurs when efforts to minimize confounding are not adequate.
In the most extreme examples, investigators classify as “smokers” all women who smoked during pregnancy, even though these women varied considerably in their level of tobacco consumption, and classify all others as “non-smokers”.
Tobacco smoke exposure is a known carcinogen [64]. Some studies have reported that maternal tobacco exposure during pregnancy is associated with increased risk of the offspring developing childhood leukemia [13,19,65,66,67], whereas others report that paternal tobacco exposure during pregnancy (a source of second-hand smoke for the mother) is associated with the child’s heightened risk of childhood leukemia [34,38,68].
Successful adjustment in multivariable models of the risk of a disorder depends on high-quality exposure data. All the adjusting in the world cannot eliminate distortions due to “social desirability responding,” such as that which occurs when respondents are truthful about their coffee consumption, but not about their tobacco exposure.
The statement, “Only at the very highest level of pre-pregnancy intake (e.g., >900 mg/day, a consumption level rarely seen in people who do not smoke) was caffeine consumption associated with increased risk of miscarriage” [49]. This raises the possibility that such high consumptions reflect the influence of tobacco exposure, which leads to the inference that the increased risk of miscarriage might also reflect residual confounding of tobacco [69]. Cigarette smoking is also a risk factor for low birth weight [70] and placenta dysfunction [71]. These associations again raise the possibility of residual confounding in studies of coffee consumption during early pregnancy and low birth weight [8,72,73], fetal growth restriction [74], and perhaps even epigenetic effects, such as childhood overweight [75].
Another challenge to eliminating confounding is posed by polymorphisms of multiple genes that influence caffeine and/or coffee consumption [76,77,78,79,80,81,82,83,84]. Some of these polymorphisms also influence the risk of diseases associated with caffeine and/or coffee consumption [85,86,87,88,89,90].
A common strategy to disentangle the contribution of genetic propensity to consume coffee/caffeine is to stratify the sample by possession of each gene variant. In essence, this amounts to exploring the caffeine/coffee association in those with and without a specific variant. However, this can be considerably more complex and pose analysis challenges. For example, alleles near genes associated with high coffee consumption are associated with adiposity, cigarette smoking, high levels of fasting insulin and glucose, low risk of hypertension, as well as favorable lipid, inflammatory, and liver enzyme profiles [82].

5. Bias 4: Reverse Causation

5.1. Coffee Consumption Changes during Early Pregnancy

Even before some women realize they are pregnant, they decrease their coffee consumption. Coffee consumption by women tends to decline as early as the 4th and 5th weeks of normal pregnancy [91,92] (see Figure 1 of [91]; and Figures 1 and 2 of [92]).
Perhaps the first signal of a viable pregnancy is the sensitivity to odors, which can be accompanied by a diminished desire for coffee and the aromas associated with it [93]. As the pregnancy signal intensifies, nausea and overt aversion to odors become increasingly evident [94].
Because women who have early nausea are at a lower risk of early fetal loss (miscarriage) than women who do not experience nausea [95,96,97], a strong pregnancy signal is seen as an indicator of a viable pregnancy, and the absence of a pregnancy signal is seen as an indicator that the situation might be suboptimal.
The decline in coffee consumption early in pregnancy among women who apparently did not intend to reduce their coffee consumption has been attributed to epiphenomena, including “aversion to tastes and smells ordinarily well tolerated.” [98]. Subsequently, the term “pregnancy signal” was used to describe some of the earliest physiologic changes associated with pregnancy, including food aversions, and (hyper)sensitivities to aromas, including those of brewed coffee and perfume [99,100,101,102]. Some now use the term, ‘pregnancy awareness’ [103].
More than half a century ago, the pregnancy signal was attributed to the high-estrogen-content of the first commercially-available oral contraceptives [104,105]. Two decades later, the pregnancy signal was linked to elevated (early morning) urine concentrations of estrone-3-glucuronide and human chorionic gonadotropin [106]. “The number of potential contributors to maternal recognition of pregnancy continues to grow and this highlights our limited appreciation of the complexity of the key molecules and signal transduction pathways that intersect during these key developmental processes.” [107]. And indeed, the number of potential contributors does continue to grow [108].
The hormonal characteristics linked to coffee consumption during pregnancy to some extent also appear to apply to consumption when women are not pregnant. For example, the lower the peak estradiol level among women prior to in vitro fertilization, the higher their caffeine consumption [109]. A similar phenomenon occurs in premenopausal women [110].

5.2. Inferences That Follow from a Weak Pregnancy Signal

If a weak pregnancy signal is an indicator of a placenta not able to produce the high concentrations of hormones and growth factors needed for fetal wellbeing and optimal growth, the fetus is at increased risk of death and limited growth. If a weak pregnancy signal also allows the gravida to continue her normal coffee consumption, then coffee will be blamed (inappropriately) for increasing the risk of miscarriage and lower birth weight. The blame is inappropriate because the level of coffee consumption is influenced by the very process that will result in potentially dire consequences. In essence, the same placental deficiencies that contribute to the adversities also fail to reduce coffee consumption. Continued pre-pregnancy level of coffee consumption is a consequence, and not a cause, of the placental deficiencies.
This is an example of “reverse causation,” which refers to situations where an antecedent is a consequence rather than a cause of illness [50,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128]. Another example has occurred in some studies that have found that people whose weight (or body mass index) is low were at heightened risk of death [129]. Low weight can be a consequence of disease that results in a loss of appetite [130]. In such situations the processes that lead to death also lead to weight loss, rather than low weight contributing to mortality risk [131].
“Reverse causation” also applies to the situation where a limited-function placenta is more likely to allow a woman to continue her usual levels of coffee consumption throughout pregnancy than is the healthy placenta that prompts a woman to reduce her coffee consumption. As a result, coffee consumption is associated with the consequences of a limited-function placenta precisely because a limited-function placenta allows higher coffee consumption than does a full-function placenta.
The limited-function placenta is associated with fetal growth restriction [132,133,134]. So is coffee/caffeine consumption [9], even if only by reverse causation.
Among the risk factors for childhood leukemias are two pregnancy phenomena, prior pregnancy loss (“fetal wastage”) [135,136,137,138] and low birth weight [139,140,141,142,143]. Both of these have been associated with continued normal (pre-pregnancy) level of coffee consumption [6,8,11,91,144,145,146,147]. To some extent, each of these (i.e., fetal wastage, low birth weight, and continued coffee consumption during pregnancy at pre-pregnancy levels) is a correlate of impaired implantation of the placenta, and a weaker pregnancy signal than occurs following a healthy implantation. Might the association between gestational coffee consumption and childhood leukemia reflect “reverse causation”?

6. Bias 5: Publication Bias

Publication (or dissemination) bias has been defined as the selective publication of studies [148,149]. This appears to happen most commonly when reviewers and editors view “positive” findings as more attractive for publication than “negative” (or non-significant) findings [150]. Publication bias can also reflect self-censorship by authors who are reluctant to continue to battle editors about the need to publish reasonably-powered “negative” studies [151]. So many other persistent influences contribute to publication bias [152,153,154] that some do not consider elimination of this bias to be feasible [155,156]. Consequently, a negative finding, such as no relationship between early pregnancy coffee consumption and risk of miscarriage, is unlikely to be attractive to editors in light of the plethora of studies reporting a positive relationship. The result is publication bias [157,158], which is especially distorting in meta-analyses [159]. These include distortion of the standardized mean difference plotted against the standard error, which can be severe when the primary studies are small [160]. In addition, asymmetry of the funnel plot might not accurately indicate publication bias [161].

7. Conclusions

I have provided comments about biases that might account for associations between maternal coffee consumption early in pregnancy and subsequent events. All of the reports of detrimental effects of coffee consumption during early pregnancy can be explained by one or more of the biases mentioned above. To what extent these biases explain away the associations between maternal early-pregnancy coffee consumption and subsequent events remains to be determined. Obviously, the more these biases can be avoided, the closer we will come to the truth. A laudable goal, but one that is difficult to achieve.


This research received no external funding.


Preparation of this report was supported by the National Coffee Association of U.S.A.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Li, J.; Zhao, H.; Song, J.M.; Zhang, J.; Tang, Y.L.; Xin, C.M. A meta-analysis of risk of pregnancy loss and caffeine and coffee consumption during pregnancy. Int. J. Gynaecol. Obstet. 2015, 130, 116–122. [Google Scholar] [CrossRef] [PubMed]
  2. Chen, L.W.; Wu, Y.; Neelakantan, N.; Chong, M.F.; Pan, A.; van Dam, R.M. Maternal caffeine intake during pregnancy and risk of pregnancy loss: A categorical and dose-response meta-analysis of prospective studies. Public Health Nutr. 2016, 19, 1233–1244. [Google Scholar] [CrossRef] [PubMed]
  3. Lyngso, J.; RamLau-Hansen, C.H.; Bay, B.; Ingerslev, H.J.; Hulman, A.; Kesmodel, U.S. Association between coffee or caffeine consumption and fecundity and fertility: A systematic review and dose-response meta-analysis. Clin. Epidemiol. 2017, 9, 699–719. [Google Scholar] [CrossRef] [PubMed]
  4. Gaskins, A.J.; Rich-Edwards, J.W.; Williams, P.L.; Toth, T.L.; Missmer, S.A.; Chavarro, J.E. Pre-pregnancy caffeine and caffeinated beverage intake and risk of spontaneous abortion. Eur. J. Nutr. 2018, 57, 107–117. [Google Scholar] [CrossRef] [PubMed]
  5. Hoyt, A.T.; Browne, M.; Richardson, S.; Romitti, P.; Druschel, C. Maternal caffeine consumption and small for gestational age births: Results from a population-based case-control study. Matern. Child. Health J. 2014, 18, 1540–1551. [Google Scholar] [CrossRef] [PubMed]
  6. Voerman, E.; Jaddoe, V.W.; Gishti, O.; Hofman, A.; Franco, O.H.; Gaillard, R. Maternal caffeine intake during pregnancy, early growth, and body fat distribution at school age. Obesity 2016, 24, 1170–1177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Xue, F.; Willett, W.C.; Rosner, B.A.; Forman, M.R.; Michels, K.B. Parental characteristics as predictors of birthweight. Hum. Reprod. 2008, 23, 168–177. [Google Scholar] [CrossRef] [PubMed]
  8. Sengpiel, V.; Elind, E.; Bacelis, J.; Nilsson, S.; Grove, J.; Myhre, R.; Haugen, M.; Meltzer, H.M.; Alexander, J.; Jacobsson, B.; et al. Maternal caffeine intake during pregnancy is associated with birth weight but not with gestational length: Results from a large prospective observational cohort study. BMC Med. 2013, 11, 42. [Google Scholar] [CrossRef] [PubMed]
  9. Rhee, J.; Kim, R.; Kim, Y.; Tam, M.; Lai, Y.; Keum, N.; Oldenburg, C.E. Maternal caffeine consumption during pregnancy and risk of low birth weight: A dose-response meta-analysis of observational studies. PLoS ONE 2015, 10, e0132334. [Google Scholar] [CrossRef] [PubMed]
  10. Greenwood, D.C.; Thatcher, N.J.; Ye, J.; Garrard, L.; Keogh, G.; King, L.G.; Cade, J.E. Caffeine intake during pregnancy and adverse birth outcomes: A systematic review and dose-response meta-analysis. Eur. J. Epidemiol. 2014, 29, 725–734. [Google Scholar] [CrossRef] [PubMed]
  11. CARE Study Group. Maternal caffeine intake during pregnancy and risk of fetal growth restriction: A large prospective observational study. BMJ 2008, 337, a2332. [Google Scholar] [CrossRef] [PubMed]
  12. Petridou, E.; Trichopoulos, D.; Kalapothaki, V.; Pourtsidis, A.; Kogevinas, M.; Kalmanti, M.; Koliouskas, D.; Kosmidis, H.; Panagiotou, J.P.; Piperopoulou, F.; et al. The risk profile of childhood leukaemia in Greece: A nationwide case-control study. Br. J. Cancer 1997, 76, 1241–1247. [Google Scholar] [CrossRef] [PubMed]
  13. Clavel, J.; Bellec, S.; Rebouissou, S.; Menegaux, F.; Feunteun, J.; Bonaiti-Pellie, C.; Baruchel, A.; Kebaili, K.; Lambilliotte, A.; Leverger, G.; et al. Childhood leukaemia, polymorphisms of metabolism enzyme genes, and interactions with maternal tobacco, coffee and alcohol consumption during pregnancy. Eur. J. Cancer Prev. 2005, 14, 531–540. [Google Scholar] [CrossRef] [PubMed]
  14. Menegaux, F.; Steffen, C.; Bellec, S.; Baruchel, A.; Lescoeur, B.; Leverger, G.; Nelken, B.; Philippe, N.; Sommelet, D.; Hemon, D.; et al. Maternal coffee and alcohol consumption during pregnancy, parental smoking and risk of childhood acute leukaemia. Cancer Detect. Prev. 2005, 29, 487–493. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Petridou, E.; Ntouvelis, E.; Dessypris, N.; Terzidis, A.; Trichopoulos, D. Maternal diet and acute lymphoblastic leukemia in young children. Cancer Epidemiol. Biomark. Prev. 2005, 14, 1935–1939. [Google Scholar] [CrossRef] [PubMed]
  16. Menegaux, F.; Ripert, M.; Hemon, D.; Clavel, J. Maternal alcohol and coffee drinking, parental smoking and childhood leukaemia: A French population-based case-control study. Paediatr. Perinat. Epidemiol. 2007, 21, 293–299. [Google Scholar] [CrossRef] [PubMed]
  17. Milne, E.; Royle, J.A.; Bennett, L.C.; de Klerk, N.H.; Bailey, H.D.; Bower, C.; Miller, M.; Attia, J.; Scott, R.J.; Kirby, M.; et al. Maternal consumption of coffee and tea during pregnancy and risk of childhood ALL: Results from an Australian case-control study. Cancer Causes Control 2011, 22, 207–218. [Google Scholar] [CrossRef] [PubMed]
  18. Bonaventure, A.; Rudant, J.; Goujon-Bellec, S.; Orsi, L.; Leverger, G.; Baruchel, A.; Bertrand, Y.; Nelken, B.; Pasquet, M.; Michel, G.; et al. Childhood acute leukemia, maternal beverage intake during pregnancy, and metabolic polymorphisms. Cancer Causes Control 2013, 24, 783–793. [Google Scholar] [CrossRef] [PubMed]
  19. Orsi, L.; Rudant, J.; Ajrouche, R.; Leverger, G.; Baruchel, A.; Nelken, B.; Pasquet, M.; Michel, G.; Bertrand, Y.; Ducassou, S.; et al. Parental smoking, maternal alcohol, coffee and tea consumption during pregnancy, and childhood acute leukemia: The ESTELLE study. Cancer Causes Control 2015, 26, 1003–1017. [Google Scholar] [CrossRef] [PubMed]
  20. Milne, E.; Greenop, K.R.; Petridou, E.; Bailey, H.D.; Orsi, L.; Kang, A.Y.; Baka, M.; Bonaventure, A.; Kourti, M.; Metayer, C.; et al. Maternal consumption of coffee and tea during pregnancy and risk of childhood ALL: A pooled analysis from the childhood leukemia international consortium. Cancer Causes Control 2018, 29, 539–550. [Google Scholar] [CrossRef] [PubMed]
  21. Sackett, D.L. Bias in analytic research. J. Chronic Dis. 1979, 32, 51–63. [Google Scholar] [CrossRef]
  22. Jahanfar, S.; Jaafar, S.H. Effects of Restricted Caffeine Intake by Mother on Fetal, Neonatal And Pregnancy Outcome. Cochrane Database Syst. Rev. 2013, 2, CD006965. [Google Scholar]
  23. Bech, B.H.; Obel, C.; Henriksen, T.B.; Olsen, J. Effect of reducing caffeine intake on birth weight and length of gestation: Randomised controlled trial. BMJ 2007, 334, 409. [Google Scholar] [CrossRef] [PubMed]
  24. Howards, P.P.; Hertz-Picciotto, I.; Bech, B.H.; Nohr, E.A.; Andersen, A.M.; Poole, C.; Olsen, J. Spontaneous abortion and a diet drug containing caffeine and ephedrine: A study within the Danish national birth cohort. PLoS ONE 2012, 7, e50372. [Google Scholar] [CrossRef] [PubMed]
  25. Connor Gorber, S.; Schofield-Hurwitz, S.; Hardt, J.; Levasseur, G.; Tremblay, M. The accuracy of self-reported smoking: A systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine Tob. Res. 2009, 11, 12–24. [Google Scholar] [CrossRef] [PubMed]
  26. Spencer, K.; Cowans, N.J. Accuracy of self-reported smoking status in first trimester aneuploidy screening. Prenat. Diagn. 2013, 33, 245–250. [Google Scholar] [CrossRef] [PubMed]
  27. Dietz, P.M.; Homa, D.; England, L.J.; Burley, K.; Tong, V.T.; Dube, S.R.; Bernert, J.T. Estimates of nondisclosure of cigarette smoking among pregnant and nonpregnant women of reproductive age in the United States. Am. J. Epidemiol. 2011, 173, 355–359. [Google Scholar] [CrossRef] [PubMed]
  28. Wehling, H.; Lusher, J. People With a Body Mass Index 30 Under-Report Their Dietary Intake: A Systematic Review. 2017. Available online: (accessed on 22 August 2018).
  29. Crowne, D.P.; Marlowe, D. A new scale of social desirability independent of psychopathology. J. Consult. Psychol. 1960, 24, 349–354. [Google Scholar] [CrossRef] [PubMed]
  30. Paulus, D.L. Two-component models of socially desirable responding. J. Personal. Soc. Psychol. 1984, 46, 598–609. [Google Scholar] [CrossRef]
  31. Tracey, T.J. A note on socially desirable responding. J. Counsel. Psychol. 2016, 63, 224–232. [Google Scholar] [CrossRef] [PubMed]
  32. Contzen, N.; De Pasquale, S.; Mosler, H.J. Over-Reporting in handwashing self-reports: Potential explanatory factors and alternative measurements. PLoS ONE 2015, 10, e0136445. [Google Scholar] [CrossRef] [PubMed]
  33. Biglan, M.; Gilpin, E.A.; Rohrbach, L.A.; Pierce, J.P. Is there a simple correction factor for comparing adolescent tobacco-use estimates from school- and home-based surveys? Nicotine Tob. Res. 2004, 6, 427–437. [Google Scholar] [CrossRef] [PubMed]
  34. Milne, E.; Greenop, K.R.; Scott, R.J.; Bailey, H.D.; Attia, J.; Dalla-Pozza, L.; de Klerk, N.H.; Armstrong, B.K. Parental prenatal smoking and risk of childhood acute lymphoblastic leukemia. Am. J. Epidemiol. 2012, 175, 43–53. [Google Scholar] [CrossRef] [PubMed]
  35. Greenop, K.R.; Miller, M.; Attia, J.; Ashton, L.J.; Cohn, R.; Armstrong, B.K.; Milne, E. Maternal consumption of coffee and tea during pregnancy and risk of childhood brain tumors: Results from an Australian case-control study. Cancer Causes Control. 2014, 25, 1321–1327. [Google Scholar] [CrossRef] [PubMed]
  36. Melchior, M.; Moffitt, T.E.; Milne, B.J.; Poulton, R.; Caspi, A. Why do children from socioeconomically disadvantaged families suffer from poor health when they reach adulthood? A life-course study. Am. J. Epidemiol. 2007, 166, 966–974. [Google Scholar] [CrossRef] [PubMed]
  37. Werler, M.M.; Pober, B.R.; Nelson, K.; Holmes, L.B. Reporting accuracy among mothers of malformed and nonmalformed infants. Am. J. Epidemiol. 1989, 129, 415–421. [Google Scholar] [CrossRef] [PubMed]
  38. Farioli, A.; Legittimo, P.; Mattioli, S.; Miligi, L.; Benvenuti, A.; Ranucci, A.; Salvan, A.; Rondelli, R.; Conter, V.; Magnani, C. Tobacco smoke and risk of childhood acute lymphoblastic leukemia: Findings from the SETIL case-control study. Cancer Causes Control. 2014, 25, 683–692. [Google Scholar] [CrossRef] [PubMed]
  39. Thomopoulos, T.P.; Ntouvelis, E.; Diamantaras, A.A.; Tzanoudaki, M.; Baka, M.; Hatzipantelis, E.; Kourti, M.; Polychronopoulou, S.; Sidi, V.; Stiakaki, E.; et al. Maternal and childhood consumption of coffee, tea and cola beverages in association with childhood leukemia: A meta-analysis. Cancer Epidemiol. 2015, 39, 1047–1059. [Google Scholar] [CrossRef] [PubMed]
  40. Cheng, J.; Su, H.; Zhu, R.; Wang, X.; Peng, M.; Song, J.; Fan, D. Maternal coffee consumption during pregnancy and risk of childhood acute leukemia: A metaanalysis. Am. J. Obstet. Gynecol. 2014, 210, 151 e1–151 e10. [Google Scholar] [CrossRef] [PubMed]
  41. The Health Consequences of Smoking—50 Years of Progress: 2014. Available online: (accessed on 29 April 2018).
  42. Yu, X.; Bao, Z.; Zou, J.; Dong, J. Coffee consumption and risk of cancers: A meta-analysis of cohort studies. BMC Cancer 2011, 11, 96. [Google Scholar] [CrossRef] [PubMed]
  43. Huang, T.B.; Guo, Z.F.; Zhang, X.L.; Zhang, X.P.; Liu, H.; Geng, J.; Yao, X.D.; Zheng, J.H. Coffee consumption and urologic cancer risk: A meta-analysis of cohort studies. Int. Urol. Nephrol. 2014, 46, 1481–1493. [Google Scholar] [CrossRef] [PubMed]
  44. Sugiyama, K.; Sugawara, Y.; Tomata, Y.; Nishino, Y.; Fukao, A.; Tsuji, I. The association between coffee consumption and bladder cancer incidence in a pooled analysis of the miyagi cohort study and ohsaki cohort study. Eur. J. Cancer Prev. 2017, 26, 125–130. [Google Scholar] [CrossRef] [PubMed]
  45. Bae, J.M.; Kim, E.H. Hormonal replacement therapy and the risk of lung cancer in women: An adaptive meta-analysis of cohort studies. J. Prev. Med. Public health 2015, 48, 280–286. [Google Scholar] [CrossRef] [PubMed]
  46. Lee, P.N.; HamLing, J.S. Environmental tobacco smoke exposure and risk of breast cancer in nonsmoking women. An updated review and meta-analysis. Inhal. Toxicol. 2016, 28, 431–454. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Pollack, A.Z.; Buck Louis, G.M.; Sundaram, R.; Lum, K.J. Caffeine consumption and miscarriage: A prospective cohort study. Fertil. Steril. 2010, 93, 304–306. [Google Scholar] [CrossRef] [PubMed]
  48. Wen, W.; Shu, X.O.; Jacobs, D.R., Jr.; Brown, J.E. The associations of maternal caffeine consumption and nausea with spontaneous abortion. Epidemiology 2001, 12, 38–42. [Google Scholar] [CrossRef] [PubMed]
  49. Tolstrup, J.S.; Kjaer, S.K.; Munk, C.; Madsen, L.B.; Ottesen, B.; Bergholt, T.; Gronbaek, M. Does caffeine and alcohol intake before pregnancy predict the occurrence of spontaneous abortion? Hum. Reprod. 2003, 18, 2704–2710. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Hahn, K.A.; Wise, L.A.; Rothman, K.J.; Mikkelsen, E.M.; Brogly, S.B.; Sorensen, H.T.; Riis, A.H.; Hatch, E.E. Caffeine and caffeinated beverage consumption and risk of spontaneous abortion. Hum. Reprod. 2015, 30, 1246–1255. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Gaskins, A.J.; Toth, T.L.; Chavarro, J.E. Prepregnancy nutrition and early pregnancy outcomes. Curr. Nutr. Rep. 2015, 4, 265–272. [Google Scholar] [CrossRef] [PubMed]
  52. Werler, M.M.; Louik, C.; Mitchell, A.A. Case-control studies for identifying novel teratogens. Am. J. Med. Genet. C Semin. Med. Genet. 2011, 157C, 201–208. [Google Scholar] [CrossRef] [PubMed]
  53. Barone, J.J.; Roberts, H.R. Caffeine consumption. Food chem. toxicol. 1996, 34, 119–129. [Google Scholar] [CrossRef]
  54. Rudolph, E.; Farbinger, A.; Konig, J. Determination of the caffeine contents of various food items within the Austrian market and validation of a caffeine assessment tool (CAT). Food Addit. Contam. 2012, 29, 1849–1860. [Google Scholar] [CrossRef] [PubMed]
  55. Lisko, J.G.; Lee, G.E.; Kimbrell, J.B.; Rybak, M.E.; Valentin-Blasini, L.; Watson, C.H. Caffeine concentrations in coffee, tea, chocolate, and energy drink flavored e-liquids. Nicotine Tob. Res. 2017, 19, 484–492. [Google Scholar] [CrossRef] [PubMed]
  56. Sanchez, J.M. Methylxanthine content in commonly consumed foods in Spain and determination of its intake during consumption. Foods 2017, 6, 109. [Google Scholar] [CrossRef] [PubMed]
  57. Bracken, M.B.; Triche, E.; Grosso, L.; Hellenbrand, K.; Belanger, K.; Leaderer, B.P. Heterogeneity in assessing self-reports of caffeine exposure: Implications for studies of health effects. Epidemiology 2002, 13, 165–171. [Google Scholar] [CrossRef] [PubMed]
  58. Ludwig, I.A.; Mena, P.; Calani, L.; Cid, C.; Del Rio, D.; Lean, M.E.; Crozier, A. Variations in caffeine and chlorogenic acid contents of coffees: What are we drinking? Food Funct. 2014, 5, 1718–1726. [Google Scholar] [CrossRef] [PubMed]
  59. Jager, K.J.; Zoccali, C.; Macleod, A.; Dekker, F.W. Confounding: What it is and how to deal with it. Kidney Int. 2008, 73, 256–260. [Google Scholar] [CrossRef] [PubMed]
  60. Plowchalk, D.R.; Rowland Yeo, K. Prediction of drug clearance in a smoking population: Modeling the impact of variable cigarette consumption on the induction of CYP1A2. Eur. J. Clin. Pharmacol. 2012, 68, 951–960. [Google Scholar] [CrossRef] [PubMed]
  61. Hukkanen, J.; Jacob, P., 3rd; Peng, M.; Dempsey, D.; Benowitz, N.L. Effect of nicotine on cytochrome P450 1A2 activity. Br. J. Clin. Pharmacol. 2011, 72, 836–838. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Bjorngaard, J.H.; Nordestgaard, A.T.; Taylor, A.E.; Treur, J.L.; Gabrielsen, M.E.; Munafo, M.R.; Nordestgaard, B.G.; Asvold, B.O.; Romundstad, P.; Davey Smith, G. Heavier smoking increases coffee consumption: Findings from a Mendelian randomization analysis. Int. J. Epidemiol. 2017, 46, 1958–1967. [Google Scholar] [CrossRef] [PubMed]
  63. De Leon, J.; Diaz, F.J.; Rogers, T.; Browne, D.; Dinsmore, L.; Ghosheh, O.H.; Dwoskin, L.P.; Crooks, P.A. A pilot study of plasma caffeine concentrations in a US sample of smoker and nonsmoker volunteers. Prog. Neuro-Psychopharmacol. Biol. Psych. 2003, 27, 165–171. [Google Scholar] [CrossRef]
  64. Hecht, S.S.; Szabo, E. Fifty years of tobacco carcinogenesis research: From mechanisms to early detection and prevention of lung cancer. Cancer Prev. Res. 2014, 7, 1–8. [Google Scholar] [CrossRef] [PubMed]
  65. Metayer, C.; Zhang, L.; Wiemels, J.L.; Bartley, K.; Schiffman, J.; Ma, X.; Aldrich, M.C.; Chang, J.S.; Selvin, S.; Fu, C.H.; et al. Tobacco smoke exposure and the risk of childhood acute lymphoblastic and myeloid leukemias by cytogenetic subtype. Cancer Epidemiol. Biomark. Prev. 2013, 22, 1600–1611. [Google Scholar] [CrossRef] [PubMed]
  66. Ferreira, J.D.; Couto, A.C.; Pombo-de-Oliveira, M.S.; Koifman, S. Pregnancy, maternal tobacco smoking, and early age leukemia in Brazil. Front. Oncol. 2012, 2, 151. [Google Scholar] [CrossRef] [PubMed]
  67. Infante-Rivard, C.; Krajinovic, M.; Labuda, D.; Sinnett, D. Parental smoking, CYP1A1 genetic polymorphisms and childhood leukemia (Quebec, Canada). Cancer Causes Control 2000, 11, 547–553. [Google Scholar] [CrossRef] [PubMed]
  68. Liu, R.; Zhang, L.; McHale, C.M.; Hammond, S.K. Paternal smoking and risk of childhood acute lymphoblastic leukemia: Systematic review and meta-analysis. J. Oncol. 2011, 2011, 16. [Google Scholar] [CrossRef] [PubMed]
  69. Pineles, B.L.; Park, E.; Samet, J.M. Systematic review and meta-analysis of miscarriage and maternal exposure to tobacco smoke during pregnancy. Am. J. Epidemiol. 2014, 179, 807–823. [Google Scholar] [CrossRef] [PubMed]
  70. Pereira, P.P.; Da Mata, F.A.; Figueiredo, A.C.; de Andrade, K.R.; Pereira, M.G. Maternal active smoking during pregnancy and low birth weight in the americas: A systematic review and meta-analysis. Nicotine Tob. Res. 2017, 19, 497–505. [Google Scholar] [CrossRef] [PubMed]
  71. Huuskonen, P.; Amezaga, M.R.; Bellingham, M.; Jones, L.H.; Storvik, M.; Hakkinen, M.; Keski-Nisula, L.; Heinonen, S.; O’Shaughnessy, P.J.; Fowler, P.A.; et al. The human placental proteome is affected by maternal smoking. Reprod. Toxicol. 2016, 63, 22–31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Eskenazi, B.; Stapleton, A.L.; Kharrazi, M.; Chee, W.Y. Associations between maternal decaffeinated and caffeinated coffee consumption and fetal growth and gestational duration. Epidemiology 1999, 10, 242–249. [Google Scholar] [CrossRef] [PubMed]
  73. Rondo, P.H.; Rodrigues, L.C.; Tomkins, A.M. Coffee consumption and intrauterine growth retardation in Brazil. Eur. J. Clin. Nutr. 1996, 50, 705–709. [Google Scholar] [PubMed]
  74. Fortier, I.; Marcoux, S.; Beaulac-Baillargeon, L. Relation of caffeine intake during pregnancy to intrauterine growth retardation and preterm birth. Am. J. Epidemiol. 1993, 137, 931–940. [Google Scholar] [CrossRef] [PubMed]
  75. Papadopoulou, E.; Botton, J.; Brantsaeter, A.L.; Haugen, M.; Alexander, J.; Meltzer, H.M.; Bacelis, J.; Elfvin, A.; Jacobsson, B.; Sengpiel, V. Maternal caffeine intake during pregnancy and childhood growth and overweight: Results from a large Norwegian prospective observational cohort study. BMJ Open 2018, 8, e018895. [Google Scholar] [CrossRef] [PubMed]
  76. Nehlig, A. Interindividual differences in caffeine metabolism and factors driving caffeine consumption. Pharmacol. Rev. 2018, 70, 384–411. [Google Scholar] [CrossRef] [PubMed]
  77. Taylor, A.E.; Davey Smith, G.; Munafo, M.R. Associations of coffee genetic risk scores with consumption of coffee, tea and other beverages in the UK Biobank. Addiction 2018, 113, 148–157. [Google Scholar] [CrossRef] [PubMed]
  78. Denden, S.; Bouden, B.; Haj Khelil, A.; Ben Chibani, J.; Hamdaoui, M.H. Gender and ethnicity modify the association between the CYP1A2 rs762551 polymorphism and habitual coffee intake: Evidence from a meta-analysis. Genet. Mol. Res. 2016, 15. [Google Scholar] [CrossRef] [PubMed]
  79. McMahon, G.; Taylor, A.E.; Davey Smith, G.; Munafo, M.R. Phenotype refinement strengthens the association of AHR and CYP1A1 genotype with caffeine consumption. PLoS ONE 2014, 9, e103448. [Google Scholar] [CrossRef] [PubMed]
  80. Pirastu, N.; Kooyman, M.; Robino, A.; van der Spek, A.; Navarini, L.; Amin, N.; Karssen, L.C.; Van Duijn, C.M.; Gasparini, P. Non-additive genome-wide association scan reveals a new gene associated with habitual coffee consumption. Sci. Rep. 2016, 6, 31590. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  81. Pirastu, N.; Kooyman, M.; Traglia, M.; Robino, A.; Willems, S.M.; Pistis, G.; d’Adamo, P.; Amin, N.; d’Eustacchio, A.; Navarini, L.; et al. Association analysis of bitter receptor genes in five isolated populations identifies a significant correlation between TAS2R43 variants and coffee liking. PLoS ONE 2014, 9, e92065. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Cornelis, M.C.; Byrne, E.M.; Esko, T.; Nalls, M.A.; Ganna, A.; Paynter, N.; Monda, K.L.; Amin, N.; Fischer, K.; Renstrom, F.; et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol. Psych. 2015, 20, 647–656. [Google Scholar] [CrossRef] [PubMed]
  83. Cornelis, M.C.; Kacprowski, T.; Menni, C.; Gustafsson, S.; Pivin, E.; Adamski, J.; Artati, A.; Eap, C.B.; Ehret, G.; Friedrich, N.; et al. Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior. Hum. Mol. Genet. 2016, 25, 5472–5482. [Google Scholar] [CrossRef] [PubMed]
  84. Cornelis, M.C.; Monda, K.L.; Yu, K.; Paynter, N.; Azzato, E.M.; Bennett, S.N.; Berndt, S.I.; Boerwinkle, E.; Chanock, S.; Chatterjee, N.; et al. Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption. PLoS Genet. 2011, 7, e1002033. [Google Scholar] [CrossRef] [PubMed]
  85. Kokaze, A.; Yoshida, M.; Ishikawa, M.; Matsunaga, N.; Karita, K.; Ochiai, H.; Shirasawa, T.; Nanri, H.; Mitsui, K.; Hoshimo, H.; et al. Mitochondrial DNA 5178 C/A polymorphism modulates the effects of coffee consumption on elevated levels of serum liver enzymes in male Japanese health check-up examinees: An exploratory cross-sectional study. J. Physiol. Anthropol. 2016, 35, 15. [Google Scholar] [CrossRef] [PubMed]
  86. Chuang, Y.H.; Lill, C.M.; Lee, P.C.; Hansen, J.; Lassen, C.F.; Bertram, L.; Greene, N.; Sinsheimer, J.S.; Ritz, B. Gene-environment interaction in parkinson’s disease: Coffee, ADORA2A, and CYP1A2. Neuroepidemiology 2016, 47, 192–200. [Google Scholar] [CrossRef] [PubMed]
  87. Casiglia, E.; Tikhonoff, V.; Albertini, F.; Favaro, J.; Montagnana, M.; Danese, E.; Finatti, F.; Benati, M.; Mazza, A.; Dal Maso, L.; et al. Caffeine intake and abstract reasoning among 1374 unselected men and women from general population. Role of the −163C>A polymorphism of CYP1A2 gene. Clin. Nutr. ESPEN 2017, 20, 52–59. [Google Scholar] [CrossRef] [PubMed]
  88. Wang, T.; Huang, T.; Kang, J.H.; Zheng, Y.; Jensen, M.K.; Wiggs, J.L.; Pasquale, L.R.; Fuchs, C.S.; Campos, H.; Rimm, E.B.; et al. Habitual coffee consumption and genetic predisposition to obesity: Gene-diet interaction analyses in three US prospective studies. BMC Med. 2017, 15, 97. [Google Scholar] [CrossRef] [PubMed]
  89. Platt, D.E.; Ghassibe-Sabbagh, M.; Salameh, P.; Salloum, A.K.; Haber, M.; Mouzaya, F.; Gauguier, D.; Al-Sarraj, Y.; El-Shanti, H.; Zalloua, P.A.; et al. Caffeine impact on metabolic syndrome components is modulated by a CYP1A2 variant. Ann. Nutr. Metabol. 2016, 68, 1–11. [Google Scholar] [CrossRef] [PubMed]
  90. Palatini, P.; Benetti, E.; Mos, L.; Garavelli, G.; Mazzer, A.; Cozzio, S.; Fania, C.; Casiglia, E. Association of coffee consumption and CYP1A2 polymorphism with risk of impaired fasting glucose in hypertensive patients. Eur. J. Epidemiol. 2015, 30, 209–217. [Google Scholar] [CrossRef] [PubMed]
  91. Cnattingius, S.; Signorello, L.B.; Anneren, G.; Clausson, B.; Ekbom, A.; Ljunger, E.; Blot, W.J.; McLaughlin, J.K.; Petersson, G.; Rane, A.; et al. Caffeine intake and the risk of first-trimester spontaneous abortion. N. Engl. J. Med. 2000, 343, 1839–1845. [Google Scholar] [CrossRef] [PubMed]
  92. Lawson, C.C.; LeMasters, G.K.; Wilson, K.A. Changes in caffeine consumption as a signal of pregnancy. Reprod. Toxicol. 2004, 18, 625–633. [Google Scholar] [CrossRef] [PubMed]
  93. Hook, E.B. Dietary cravings and aversions during pregnancy. Am. J. Clin. Nutr. 1978, 31, 1355–1362. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Weigel, M.M.; Coe, K.; Castro, N.P.; Caiza, M.E.; Tello, N.; Reyes, M. Food aversions and cravings during early pregnancy: Association with nausea and vomiting. Ecol. Food Nutr. 2011, 50, 197–214. [Google Scholar] [CrossRef] [PubMed]
  95. Weigel, R.M.; Weigel, M.M. Nausea and vomiting of early pregnancy and pregnancy outcome. A meta-analytical review. Br. J. Obstet. Gynaecol. 1989, 96, 1312–1318. [Google Scholar] [CrossRef] [PubMed]
  96. Sayle, A.E.; Wilcox, A.J.; Weinberg, C.R.; Baird, D.D. A prospective study of the onset of symptoms of pregnancy. J. Clin. Epidemiol. 2002, 55, 676–680. [Google Scholar] [CrossRef]
  97. Chan, R.L.; Olshan, A.F.; Savitz, D.A.; Herring, A.H.; Daniels, J.L.; Peterson, H.B.; Martin, S.L. Severity and duration of nausea and vomiting symptoms in pregnancy and spontaneous abortion. Hum. Reprod. 2010, 25, 2907–2912. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Stein, Z.; Susser, M. Miscarriage, caffeine, and the epiphenomena of pregnancy: The causal model. Epidemiology 1991, 2, 163–167. [Google Scholar] [PubMed]
  99. Geisert, R.D.; Ross, J.W.; Ashworth, M.D.; White, F.J.; Johnson, G.A.; DeSilva, U. Maternal recognition of pregnancy signal or endocrine disruptor: The two faces of oestrogen during establishment of pregnancy in the pig. Soc. Reprod. Fertil. Suppl. 2006, 62, 131–145. [Google Scholar] [PubMed]
  100. Wollenhaupt, K.; Brussow, K.P.; Tiemann, U.; Tomek, W. The embryonic pregnancy signal oestradiol influences gene expression at the level of translational initiation in porcine endometrial cells. Reprod. Domest. Anim. 2007, 42, 167–175. [Google Scholar] [CrossRef] [PubMed]
  101. Brent, R.L.; Christian, M.S.; Diener, R.M. Evaluation of the reproductive and developmental risks of caffeine. Birth Defects Res. 2011, 92, 152–187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Porciuncula, L.O.; Sallaberry, C.; Mioranzza, S.; Botton, P.H.; Rosemberg, D.B. The Janus face of caffeine. Neurochem. Int. 2013, 63, 594–609. [Google Scholar] [CrossRef] [PubMed]
  103. Peacock, A.; Hutchinson, D.; Wilson, J.; McCormack, C.; Bruno, R.; Olsson, C.A.; Allsop, S.; Elliott, E.; Burns, L.; Mattick, R.P. Adherence to the caffeine intake guideline during pregnancy and birth outcomes: A prospective cohort study. Nutrients 2018, 10, 319. [Google Scholar] [CrossRef] [PubMed]
  104. Russell, M.; Ramcharan, S. Oral contraceptive estrogen content and adverse effects. Can. Fam. Phys. Med. 1987, 33, 445–460. [Google Scholar]
  105. Speroff, L. The formulation of oral contraceptives: Does the amount of estrogen make any clinical difference? Johns. Hopkins med. J. 1982, 150, 170–176. [Google Scholar] [PubMed]
  106. Lawson, C.C.; LeMasters, G.K.; Levin, L.S.; Liu, J.H. Pregnancy hormone metabolite patterns, pregnancy symptoms, and coffee consumption. Am. J. Epidemiol. 2002, 156, 428–437. [Google Scholar] [CrossRef] [PubMed]
  107. Spencer, T.E.; Johnson, G.A.; Bazer, F.W.; Burghardt, R.C.; Palmarini, M. Pregnancy recognition and conceptus implantation in domestic ruminants: Roles of progesterone, interferons and endogenous retroviruses. Reprod. Fertil. Dev. 2007, 19, 65–78. [Google Scholar] [CrossRef] [PubMed]
  108. Brooks, K.; Burns, G.; Spencer, T.E. Conceptus elongation in ruminants: Roles of progesterone, prostaglandin, interferon tau and cortisol. J. Anim. Sci. Biotechnol. 2014, 5, 53. [Google Scholar] [CrossRef] [PubMed]
  109. Choi, J.H.; Ryan, L.M.; Cramer, D.W.; Hornstein, M.D.; Missmer, S.A. Effects of caffeine consumption by women and men on the outcome of in vitro fertilization. J. Caff. Res. 2011, 1, 29–34. [Google Scholar] [CrossRef] [PubMed]
  110. Sisti, J.S.; Hankinson, S.E.; Caporaso, N.E.; Gu, F.; Tamimi, R.M.; Rosner, B.; Xu, X.; Ziegler, R.; Eliassen, A.H. Caffeine, coffee, and tea intake and urinary estrogens and estrogen metabolites in premenopausal women. Cancer Epidemiol. Biomark. Prev. 2015, 24, 1174–1183. [Google Scholar] [CrossRef] [PubMed]
  111. Flegal, K.M.; Graubard, B.I.; Williamson, D.F.; Cooper, R.S. Reverse causation and illness-related weight loss in observational studies of body weight and mortality. Am. J. Epidemiol 2011, 173, 1–9. [Google Scholar] [CrossRef] [PubMed]
  112. Kim, T.J.; von dem Knesebeck, O. Income and obesity: What is the direction of the relationship? A systematic review and meta-analysis. BMJ open 2018, 8, e019862. [Google Scholar] [PubMed]
  113. Maselko, J.; Hayward, R.D.; Hanlon, A.; Buka, S.; Meador, K. Religious service attendance and major depression: A case of reverse causality? Am. J. Epidemiol. 2012, 175, 576–583. [Google Scholar] [CrossRef] [PubMed]
  114. Kalantar-Zadeh, K.; Block, G.; Horwich, T.; Fonarow, G.C. Reverse epidemiology of conventional cardiovascular risk factors in patients with chronic heart failure. J. Am. Coll. Cardiol. 2004, 43, 1439–1444. [Google Scholar] [CrossRef] [PubMed]
  115. Stokes, A.; Preston, S.H. Smoking and reverse causation create an obesity paradox in cardiovascular disease. Obesity 2015, 23, 2485–2490. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Kerger, B.D.; Scott, P.K.; Pavuk, M.; Gough, M.; Paustenbach, D.J. Re-analysis of Ranch Hand study supports reverse causation hypothesis between dioxin and diabetes. Crit. Rev. toxicol. 2012, 42, 669–687. [Google Scholar] [CrossRef] [PubMed]
  117. Januar, V.; Desoye, G.; Novakovic, B.; Cvitic, S.; Saffery, R. Epigenetic regulation of human placental function and pregnancy outcome: Considerations for causal inference. Am. J. Obstet. Gynecol. 2015, 213, S182–S196. [Google Scholar] [CrossRef] [PubMed]
  118. Ahiadeke, C.; Gurak, D.T.; Schwager, S.J. Breastfeeding behavior and infant survival with emphasis on reverse causation bias: Some evidence from Nigeria. Soc. Biol. 2000, 47, 94–113. [Google Scholar] [CrossRef] [PubMed]
  119. Fussman, C.; Todem, D.; Forster, J.; Arshad, H.; Urbanek, R.; Karmaus, W. Cow’s milk exposure and asthma in a newborn cohort: Repeated ascertainment indicates reverse causation. J. Asthma 2007, 44, 99–105. [Google Scholar] [CrossRef] [PubMed]
  120. Grassi, M.; Assanelli, D.; Pezzini, A. Direct, reverse or reciprocal causation in the relation between homocysteine and ischemic heart disease. Thromb. Res. 2007, 120, 61–69. [Google Scholar] [CrossRef] [PubMed]
  121. Kummeling, I.; Thijs, C. Reverse causation and confounding-by-indication: Do they or do they not explain the association between childhood antibiotic treatment and subsequent development of respiratory illness? Clin. Exp. Allergy 2008, 38, 1249–1251. [Google Scholar] [CrossRef] [PubMed]
  122. Lodge, C.J.; Lowe, A.J.; Dharmage, S.C. Is reverse causation responsible for the link between duration of breastfeeding and childhood asthma? Am. J. Respir. Crit. Care Med. 2008, 178, 994. [Google Scholar] [CrossRef] [PubMed]
  123. Luciano, M.; Marioni, R.E.; Gow, A.J.; Starr, J.M.; Deary, I.J. Reverse causation in the association between C-reactive protein and fibrinogen levels and cognitive abilities in an aging sample. Psychosom. Med. 2009, 71, 404–409. [Google Scholar] [CrossRef] [PubMed]
  124. Kusunoki, T.; Morimoto, T.; Nishikomori, R.; Yasumi, T.; Heike, T.; Mukaida, K.; Fujii, T.; Nakahata, T. Breastfeeding and the prevalence of allergic diseases in schoolchildren: Does reverse causation matter? Pediatr. Allergy Immunol 2010, 21, 60–66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Barbui, C.; Gastaldon, C.; Cipriani, A. Benzodiazepines and risk of dementia: True association or reverse causation? Epidemiol. Psychiatr. Sci. 2013, 22, 307–308. [Google Scholar] [CrossRef] [PubMed]
  126. Brunner, E.J.; Shipley, M.J.; Britton, A.R.; Stansfeld, S.A.; Heuschmann, P.U.; Rudd, A.G.; Wolfe, C.D.; Singh-Manoux, A.; Kivimaki, M. Depressive disorder, coronary heart disease, and stroke: Dose-response and reverse causation effects in the Whitehall II cohort study. Eur. J. Prev. Cardiol. 2014, 21, 340–346. [Google Scholar] [CrossRef] [PubMed]
  127. Duncan, G.E.; Mills, B.; Strachan, E.; Hurvitz, P.; Huang, R.; Moudon, A.V.; Turkheimer, E. Stepping towards causation in studies of neighborhood and environmental effects: How twin research can overcome problems of selection and reverse causation. Health Place 2014, 27, 106–111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  128. Belfort, M.B.; Kuban, K.C.; O’Shea, T.M.; Allred, E.N.; Ehrenkranz, R.A.; Engelke, S.C.; Leviton, A. Weight status in the first 2 years of life and neurodevelopmental impairment in extremely low gestational age newborns. J. Pediatr. 2016, 168, 30–35. [Google Scholar] [CrossRef] [PubMed]
  129. Niedziela, J.; Hudzik, B.; Niedziela, N.; Gasior, M.; Gierlotka, M.; Wasilewski, J.; Myrda, K.; Lekston, A.; Polonski, L.; Rozentryt, P. The obesity paradox in acute coronary syndrome: A meta-analysis. Eur. J. Epidemiol. 2014, 29, 801–812. [Google Scholar] [CrossRef] [PubMed]
  130. Kalantar-Zadeh, K.; Horwich, T.B.; Oreopoulos, A.; Kovesdy, C.P.; Younessi, H.; Anker, S.D.; Morley, J.E. Risk factor paradox in wasting diseases. Curr. Opin. Clin. Nutr. Metabol. Care 2007, 10, 433–442. [Google Scholar] [CrossRef] [PubMed]
  131. Habbu, A.; Lakkis, N.M.; Dokainish, H. The obesity paradox: Fact or fiction? Am. J. Cardiol. 2006, 98, 944–948. [Google Scholar] [CrossRef] [PubMed]
  132. Krishna, U.; Bhalerao, S. Placental insufficiency and fetal growth restriction. J. Obstet. Gynaecol. India 2011, 61, 505–511. [Google Scholar] [CrossRef] [PubMed]
  133. Parra-Saavedra, M.; Simeone, S.; Triunfo, S.; Crovetto, F.; Botet, F.; Nadal, A.; Gratacos, E.; Figueras, F. Correlation between histological signs of placental underperfusion and perinatal morbidity in late-onset small-for-gestational-age fetuses. Ultrasound Obstet. Gynecol. 2015, 45, 149–155. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  134. Kingdom, J.C.; Audette, M.C.; Hobson, S.R.; Windrim, R.C.; Morgen, E. A placenta clinic approach to the diagnosis and management of fetal growth restriction. Am. J. Obstet. Gynecol. 2018, 218, S803–S817. [Google Scholar] [CrossRef] [PubMed]
  135. Karalexi, M.A.; Skalkidou, A.; Thomopoulos, T.P.; Belechri, M.; Biniaris-Georgallis, S.I.; Bouka, E.; Baka, M.; Hatzipantelis, E.; Kourti, M.; Polychronopoulou, S.; et al. History of maternal fetal loss and childhood leukaemia risk in subsequent offspring: Differentials by miscarriage or stillbirth history and disease subtype. Paediatr. Perinat. Epidemiol. 2015, 29, 453–461. [Google Scholar] [CrossRef] [PubMed]
  136. Rudant, J.; Amigou, A.; Orsi, L.; Althaus, T.; Leverger, G.; Baruchel, A.; Bertrand, Y.; Nelken, B.; Plat, G.; Michel, G.; et al. Fertility treatments, congenital malformations, fetal loss, and childhood acute leukemia: The ESCALE study (SFCE). Pediatr. Blood Cancer 2013, 60, 301–308. [Google Scholar] [CrossRef] [PubMed]
  137. Ajrouche, R.; Rudant, J.; Orsi, L.; Petit, A.; Baruchel, A.; Nelken, B.; Pasquet, M.; Michel, G.; Bergeron, C.; Ducassou, S.; et al. Maternal reproductive history, fertility treatments and folic acid supplementation in the risk of childhood acute leukemia: The ESTELLE study. Cancer Causes Control 2014, 25, 1283–1293. [Google Scholar] [CrossRef] [PubMed]
  138. Yeazel, M.W.; Buckley, J.D.; Woods, W.G.; Ruccione, K.; Robison, L.L. History of maternal fetal loss and increased risk of childhood acute leukemia at an early age. A report from the Childrens Cancer Group. Cancer 1995, 75, 1718–1727. [Google Scholar] [CrossRef]
  139. O’Neill, K.A.; Murphy, M.F.; Bunch, K.J.; Puumala, S.E.; Carozza, S.E.; Chow, E.J.; Mueller, B.A.; McLaughlin, C.C.; Reynolds, P.; Vincent, T.J.; et al. Infant birthweight and risk of childhood cancer: International population-based case control studies of 40,000 cases. Int. J. Epidemiol. 2015, 44, 153–168. [Google Scholar] [CrossRef] [PubMed]
  140. Gruhn, B.; Taub, J.W.; Ge, Y.; Beck, J.F.; Zell, R.; Hafer, R.; Hermann, F.H.; Debatin, K.M.; Steinbach, D. Prenatal origin of childhood acute lymphoblastic leukemia, association with birth weight and hyperdiploidy. Leuk 2008, 22, 1692–1697. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  141. O’Neill, K.A.; Bunch, K.J.; Vincent, T.J.; Spector, L.G.; Moorman, A.V.; Murphy, M.F. Immunophenotype and cytogenetic characteristics in the relationship between birth weight and childhood leukemia. Pediatr. Blood Cancer 2012, 58, 7–11. [Google Scholar] [CrossRef] [PubMed]
  142. Caughey, R.W.; Michels, K.B. Birth weight and childhood leukemia: A meta-analysis and review of the current evidence. Int. J. Cancer. J. Int. Du cancer 2009, 124, 2658–2670. [Google Scholar] [CrossRef] [PubMed]
  143. Hjalgrim, L.L.; Rostgaard, K.; Hjalgrim, H.; Westergaard, T.; Thomassen, H.; Forestier, E.; Gustafsson, G.; Kristinsson, J.; Melbye, M.; Schmiegelow, K. Birth weight and risk for childhood leukemia in Denmark, Sweden, Norway, and Iceland. J. Nati. Cancer Inst. 2004, 96, 1549–1556. [Google Scholar] [CrossRef] [PubMed]
  144. Kuzniewicz, M.W.; Wi, S.; Qian, Y.; Walsh, E.M.; Armstrong, M.A.; Croen, L.A. Prevalence and neonatal factors associated with autism spectrum disorders in preterm infants. J. Pediatr. 2014, 164, 20–25. [Google Scholar] [CrossRef] [PubMed]
  145. Chen, L.W.; Wu, Y.; Neelakantan, N.; Chong, M.F.; Pan, A.; van Dam, R.M. Maternal caffeine intake during pregnancy is associated with risk of low birth weight: A systematic review and dose-response meta-analysis. BMC Med. 2014, 12, 174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  146. Bakker, R.; Steegers, E.A.; Obradov, A.; Raat, H.; Hofman, A.; Jaddoe, V.W. Maternal caffeine intake from coffee and tea, fetal growth, and the risks of adverse birth outcomes: The Generation R Study. Am. J. Clin. Nutr. 2010, 91, 1691–1698. [Google Scholar] [CrossRef] [PubMed]
  147. Barry, D. Differential recall bias and spurious associations in case/control studies. Stat. Med. 1996, 15, 2603–2616. [Google Scholar] [CrossRef]
  148. Muller, K.F.; Briel, M.; D’Amario, A.; Kleijnen, J.; Marusic, A.; Wager, E.; Antes, G.; von Elm, E.; Lang, B.; Motschall, E.; et al. Defining publication bias: Protocol for a systematic review of highly cited articles and proposal for a new framework. Syst. Rev. 2013, 2, 34. [Google Scholar] [CrossRef] [PubMed]
  149. Ioannidis, J.P.; Munafo, M.R.; Fusar-Poli, P.; Nosek, B.A.; David, S.P. Publication and other reporting biases in cognitive sciences: Detection, prevalence, and prevention. Trends Cogn. Sci. 2014, 18, 235–241. [Google Scholar] [CrossRef] [PubMed]
  150. Dwan, K.; Gamble, C.; Williamson, P.R.; Kirkham, J.J. Systematic review of the empirical evidence of study publication bias and outcome reporting bias—An updated review. PLoS ONE 2013, 8, e66844. [Google Scholar] [CrossRef] [PubMed]
  151. Connor, J.T. Positive reasons for publishing negative findings. Am. J. Gastroenterol. 2008, 103, 2181–2183. [Google Scholar] [CrossRef] [PubMed]
  152. Post, R.M. Biased public health perspective on depression treatment: Media bias on publication bias. Am. J. Psych. 2009, 166, 934–935. [Google Scholar] [CrossRef] [PubMed]
  153. Mathew, S.J.; Charney, D.S. Publication bias and the efficacy of antidepressants. Am. J. Psych. 2009, 166, 140–145. [Google Scholar] [CrossRef] [PubMed]
  154. Bowden, J.; Jackson, D.; Thompson, S.G. Modelling multiple sources of dissemination bias in meta-analysis. Stat. Med. 2010, 29, 945–955. [Google Scholar] [CrossRef] [PubMed]
  155. Meerpohl, J.J.; Schell, L.K.; Bassler, D.; Gallus, S.; Kleijnen, J.; Kulig, M.; La Vecchia, C.; Marusic, A.; Ravaud, P.; Reis, A.; et al. Evidence-informed recommendations to reduce dissemination bias in clinical research: Conclusions from the OPEN (Overcome failure to Publish nEgative fiNdings) project based on an international consensus meeting. BMJ Open 2015, 5, e006666. [Google Scholar] [CrossRef] [PubMed]
  156. Carroll, H.A.; Toumpakari, Z.; Johnson, L.; Betts, J.A. The perceived feasibility of methods to reduce publication bias. PLoS ONE 2017, 12, e0186472. [Google Scholar] [CrossRef] [PubMed]
  157. Nissen, S.B.; Magidson, T.; Gross, K.; Bergstrom, C.T. Publication bias and the canonization of false facts. eLife 2016, 5, e21451. [Google Scholar] [CrossRef] [PubMed]
  158. Young, N.S.; Ioannidis, J.P.; Al-Ubaydli, O. Why current publication practices may distort science. PLoS Med. 2008, 5, e201. [Google Scholar] [CrossRef] [PubMed]
  159. Kicinski, M.; Springate, D.A.; Kontopantelis, E. Publication bias in meta-analyses from the Cochrane database of systematic reviews. Stat. Med. 2015, 34, 2781–2793. [Google Scholar] [CrossRef] [PubMed]
  160. Zwetsloot, P.P.; Van Der Naald, M.; Sena, E.S.; Howells, D.W.; IntHout, J.; De Groot, J.A.; Chamuleau, S.A.; MacLeod, M.R.; Wever, K.E. Standardized mean differences cause funnel plot distortion in publication bias assessments. eLife 2017, 6, e24260. [Google Scholar] [CrossRef] [PubMed]
  161. Lau, J.; Ioannidis, J.P.; Terrin, N.; Schmid, C.H.; Olkin, I. The case of the misleading funnel plot. BMJ 2006, 333, 597–600. [Google Scholar] [CrossRef] [PubMed] [Green Version]

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Leviton, A. Biases Inherent in Studies of Coffee Consumption in Early Pregnancy and the Risks of Subsequent Events. Nutrients 2018, 10, 1152.

AMA Style

Leviton A. Biases Inherent in Studies of Coffee Consumption in Early Pregnancy and the Risks of Subsequent Events. Nutrients. 2018; 10(9):1152.

Chicago/Turabian Style

Leviton, Alan. 2018. "Biases Inherent in Studies of Coffee Consumption in Early Pregnancy and the Risks of Subsequent Events" Nutrients 10, no. 9: 1152.

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