The impact of receiving predictive genetic information about Lynch syndrome on individual colonoscopy and smoking behaviors

Abstract

Background

This study investigated whether receiving the results of predictive genetic testing for Lynch syndrome—indicating the presence or absence of an inherited predisposition to various cancers, including colorectal cancer—was associated with change in individual colonoscopy and smoking behaviours, which could prevent colorectal cancer.

Methods

The study population included individuals with no previous diagnosis of colorectal cancer, whose families had already-identified deleterious mutations in the mismatch repair or EPCAM genes. Hypotheses were generated from a simple health economics model and tested against individual-level panel data from the Australasian Colorectal Cancer Family Registry.

Results

The empirical analysis revealed evidence consistent with some of the hypotheses, with a higher likelihood of undergoing colonoscopy in those who discovered their genetic predisposition to colorectal cancer and a lower likelihood of quitting smoking in those who discovered their lack thereof.

Conclusion

Predictive genetic information about Lynch syndrome was associated with change in individual colonoscopy and smoking behaviours but not necessarily in ways to improve population health.

Impact

The study findings suggest that the impact of personalized medicine on disease prevention is intricate, warranting further analyses to determine the net benefits and costs.

INTRODUCTION

Personalized medicine refers to the tailoring of preventive, diagnostic, and therapeutic interventions to the characteristics of individuals, using advanced biomedical technologies (1). Its ability to identify genetic susceptibilities to preventable diseases, obtain unequivocal diagnostic results, and tailor drug therapies on an individual basis promises to revolutionize healthcare and improve population health (2,3).

Many promises of personalized medicine have been realized, especially in cancer treatment (4). Its impact in cancer prevention, such as predictive genetic testing—assessing whether individuals with a family history, but no previous diagnosis, of a genetic disorder carry their family’s deleterious genetic mutation and are at elevated risk of developing the disease in the future—has also been demonstrated. For example, positive results from predictive genetic testing for hereditary breast cancer or Lynch syndrome are associated with increased adherence to risk-reducing strategies, such as prophylactic surgery or surveillance (5), although behavioural change from predictive genetic information has generally been deemed less than expected (6,7).

Building on this literature, this study investigated whether receiving a positive or negative result from predictive genetic testing for Lynch syndrome was associated with change in individual health behaviours in Australasians. The lifetime probability of developing colorectal cancer is estimated to be 54% to 74% for men and 30% to 52% for women with Lynch syndrome, compared to 5% to 6% for the general population (8,9,10). Various health behaviours have been demonstrated to reduce the risk of hereditary colorectal cancer in Lynch syndrome families, such as colonoscopy use (11,12), smoking (13,14), and other lifestyle behaviours, including diet (15). Based on data availability, this study focused on colonoscopy and smoking behaviours. Colonoscopy is recommended for the prevention and early detection of colorectal cancer for individuals at elevated risk (11), and its use has been shown to change upon genetic testing for Lynch syndrome (16,17,18,19,20,21,22,23). Smoking represents a broader array of health behaviours that also affect the incidence of colorectal cancer, but the impact of genetic testing for Lynch syndrome on smoking, or any other lifestyle behaviours, is yet to be assessed.

From a simple health economics model, we generated hypotheses on the impact of the new information that the advanced biomedical technology provides. We then tested the hypotheses against individual-level panel data. Our study distinguished individuals with a positive genetic predisposition (“carriers”) from those without (“non-carriers”), as well as individuals who received their genetic testing results (“receivers”) from those who did not (“non-receivers”).

In what follows, we present our hypotheses, data, and empirical strategy. We then describe our results. The final section discusses and concludes.

MATERIALS AND METHODS

Hypotheses

We developed a model of utility maximization under uncertainty that incorporated Ehrlich and Becker’s concepts of self-insurance and self-protection (24). The full model is presented in Supplementary Materials and Methods. Briefly, our model characterized the occurrence of colorectal cancer as a probabilistic event, where carriers have a higher probability of developing colorectal cancer, compared to non-carriers (8,9). The probability of developing colorectal cancer was defined as a combination of exogenously-determined genetics and endogenously-determined health behaviours, namely, colonoscopy use and smoking. Receivers, unlike non-receivers, have an opportunity to change their behaviours, based on the results that are returned to them. Our model predicts that carriers who receive their genetic information are more likely to undergo colonoscopy but less likely to smoke, compared to carriers who do not receive their genetic information, as the opportunity cost of foregoing healthy behaviours rises with increasing risk. Our model also predicts that non-carriers who receive their genetic information are less likely to undergo colonoscopy but more likely to smoke, compared to non-carriers who do not receive their genetic information, as the opportunity cost of investing in healthy behaviours rises with decreasing risk. The predictions were tested empirically.

Data Source

We used data from the Australasian Colorectal Cancer Family Registry (ACCFR) for the statistical analysis. Ethics approval for the analysis was obtained from the Office of Research Ethics at the University of Toronto, Canada. Detailed descriptions of the ACCFR were provided by Newcomb et al. (25) and Jenkins et al. (26). Here, we provide an overview of the ACCFR registrant recruitment, data collection, and genetic testing.

ACCFR Registrant Recruitment

Between 1997 and 2007, the ACCFR used several recruitment strategies. Individuals who were diagnosed with their first colorectal cancer between the ages of 18 and 59 years (1997–2001) or 18 and 49 years (2001–2006) were identified from the population-based Victorian Cancer Registry. Cases of colorectal cancer or other Lynch syndrome-related cancers (i.e., of the endometrium, stomach, small intestine, urinary tract, or central nervous system) were also identified from seven family cancer clinics across Australia and New Zealand. From these clinics, individuals at high risk of Lynch syndrome were also identified, using the Amsterdam criteria (27). All identified individuals, known as probands, were invited to register with the ACCFR. The probands’ first- and second-degree relatives, as well as other relatives with previous diagnosis of any cancer, were also invited to register with the ACCFR (25). Therefore, the ACCFR registrants comprised individuals with previous diagnosis of colorectal cancer or other Lynch syndrome-related cancers and their first-, second-, and more distant relatives with or without any cancer.

ACCFR Data Collection

The ACCFR registrants were asked to complete an interviewer-administered questionnaire at enrollment (“baseline”) and after five years (“follow-up”). The baseline questionnaire asked about the registrants’ socio-demographic factors (e.g., age, sex, marital status, and education), health behaviours (e.g., colonoscopy use and smoking), and medical conditions (e.g., previous cancer diagnosis). The follow-up questionnaire obtained updates on the registrants’ health behaviours and medical conditions. All reported incidents of Lynch syndrome-related cancers were verified against medical records (25,26).

ACCFR Genetic Testing

The ACCFR registrants were asked to provide a blood sample for the purpose of conducting research and not for the purpose of genetic testing per se. Instead, blood samples of the registrants whose families had previously-identified deleterious mutations in the MMR or EPCAM genes were tested for their families’ genetic mutations by the ACCFR. Therefore, the registrants did not choose to undergo genetic testing, and the availability of their results at the ACCFR was free of potential selection bias associated with the choice to undergo genetic testing. The registrants were informed of the availability of their results and referred to a genetic counseling clinic to receive formal re-testing and their results free of charge (28,29,30).

Study Participant Selection

The study population for the analysis included all registrants of the ACCFR that would have been eligible for predictive genetic testing for Lynch syndrome, having: 1) no previous diagnosis of colorectal cancer at follow-up so that the testing would be truly predictive; and 2) a family with a previously-identified deleterious mutation in the MMR or EPCAM genes so that testing for that mutation would conclusively distinguish carriers from non-carriers. A total of 1,753 registrants of the ACCFR satisfied the conditions and had been tested and identified as either carriers or non-carriers of their families’ deleterious mutations in the MMR or EPCAM genes. However, 1,246 of the 1,753 individuals had insufficient information on whether or when their genetic testing results had been disclosed to them and were excluded from the analysis. Therefore, the study sample for the analysis comprised 507 individuals who had sufficient information on whether or when their results had been disclosed to them.

Measures and Statistical Analysis

We used logit regression to estimate the probability that an individual underwent colonoscopy or smoked between baseline and follow-up (i.e., yes/no), I_B-F or S_B-F, respectively, as a function of: the carrier status (i.e., carrier/non-carrier), G; the receiver status at follow-up (i.e., receiver/non-receiver), R_F; previous cancer diagnosis at follow-up (i.e., yes/no), H_F; the interaction of the carrier and receiver statuses and previous cancer diagnosis at follow-up, G x R_F, G x H_F, R_F x H_F, and G x R_F x H_F; a vector of the socio-demographic (i.e., age at follow-up, sex, marital status, and education) and colorectal cancer awareness (i.e., degree of relatedness to family probands) factors, D_F; and colonoscopy use or smoking at baseline (i.e., yes/no), I_B or S_B, respectively. In other words:

$$\mathit{\text{logit}}\mathit{(}P\mathit{(}{I}_{B-F}\mathit{)}\mathit{)}={\alpha }_{\mathit{0}}+{\alpha }_{\mathit{1}}G+{\alpha }_{\mathit{2}}{R}_F+{\alpha }_{\mathit{3}}{H}_F+{\alpha }_{\mathit{4}}Gx{R}_F+{\alpha }_{\mathit{5}}Gx{H}_F+{\alpha }_{\mathit{6}}{R}_{F}x{H}_F+{\alpha }_{\mathit{7}}Gx{R}_{F}x{H}_F+{\alpha }_{\mathit{8}}{D}_F+{\alpha }_{\mathit{9}}{I}_B+{\epsilon }_I$$

$$\mathit{\text{logit}}\mathit{(}P\mathit{(}{S}_{B-F}\mathit{)}\mathit{)}={\beta }_{\mathit{0}}+{\beta }_{\mathit{1}}G+{\beta }_{\mathit{2}}{R}_F+{\beta }_{\mathit{3}}{H}_F+{\beta }_{\mathit{4}}Gx{R}_F+{\beta }_{\mathit{5}}Gx{H}_F+{\beta }_{\mathit{6}}{R}_{F}x{H}_F+{\beta }_{\mathit{7}}Gx{R}_{F}x{H}_F+{\beta }_{\mathit{8}}{D}_F+{\beta }_{\mathit{9}}{S}_B+{\epsilon }_S$$

Table 1 provides the full variable list, including the scales and sources of the variables.

Table 1.

Variable list

Variable	Scale	Source (question asked)
Outcomes
Colonoscopy use between   baseline and follow-up	Binomial: Yes/No	Follow-up ACCFR questionnaire   (“Since the date of your last interview (baseline), have you had a colonoscopy?”)
Colonoscopy use within two   years of follow-up	Binomial: Yes/No	Follow-up ACCFR questionnaire   (“Since the date of your last interview (baseline), have you had a colonoscopy?”   and “What was your age when you had your most recent (colonoscopy) test?”)
Smoking between baseline   and follow-up	Binomial: Yes/No	Follow-up ACCFR questionnaire   (“Since the date of your last interview (baseline), have you ever smoked a cigarette   a day for three months or longer?”)
Smoking at follow-up	Binomial: Yes/No	Follow-up ACCFR questionnaire   (“Do you currently smoke at least one cigarette a day?”)
Exposures
Carrier status	Binomial: Carrier/Non-carrier	Genetic testing results available at the ACCFR
Receiver status at follow-up	Binomial: Receiver/Non-receiver	Dates of genetic counseling sessions provided from the ACCFR-referred clinics and   available at the ACCFR against dates of the ACCFR questionnaire administration
		Records of previous receipts of genetic testing results from elsewhere before follow-up   or refusals to receive genetic testing results from the ACCFR-referred clinics
Previous cancer diagnosis at   follow-up	Binomial: Yes/No	Baseline ACCFR questionnaire   (“Has a doctor ever told you that you had cancer, leukemia, or a malignant   tumour?”)
		Follow-up ACCFR questionnaire   (“Since the date of your last interview (baseline), has a doctor told you that you had   any type of cancer, leukemia, or a malignant tumour?”)
		Pathology reports, hospital records, and cancer registries accessed by the ACCFR
Controls
Age	Continuous: Years	Baseline and follow-up ACCFR questionnaires   (“What is your age?”)
Sex	Binomial: Female/Male	Baseline ACCFR questionnaire   (“Are you male or female?”)
Marital status	Binomial: Married or common-law/Single	Baseline ACCFR questionnaire   (“Marital status?”)
Education	Categorical: Less than high school/High, vocational,   or training school/College, university, or more	Baseline ACCFR questionnaire:   (“What is the highest level of education that you have completed?”)
Degree of relatedness to   family probands	Categorical: Self or identical twin/1st/2nd/3rd/Higher   degree	Family information collected at enrollment into the ACCFR
Colonoscopy use before   baseline	Binomial: Yes/No	Baseline ACCFR questionnaire   (“Have you ever had a colonoscopy?”)
Colonoscopy use within two   years of baseline	Binomial: Yes/No	Baseline ACCFR questionnaire   (“Have you ever had a colonoscopy?” and “What was your age when you last had   this (colonoscopy) test?”)
Smoking before baseline	Binomial: Yes/No	Baseline ACCFR questionnaire   (“Have you ever smoked at least one cigarette a day for three months or longer?”)
Smoking at baseline	Binomial: Yes/No	Baseline ACCFR questionnaire   (“Do you currently smoke at least one cigarette a day?”)

Colonoscopy use and smoking between baseline and follow-up (i.e., I_B-F and S_B-F), as well as before baseline (i.e., I_B and S_B), were defined in terms of propensity (i.e., yes/no) to undertake the behaviours. We adjusted for the respective behaviours at baseline (i.e., I_B or S_B), following suggestions by others to account for any baseline imbalance in the outcome variables (31). We repeated the analysis on colonoscopy use within two years of follow-up and smoking at follow-up, adjusting for colonoscopy use within two years of baseline and smoking at baseline, respectively, to ensure the relevance of the behaviours at those time-points. We also conducted subgroup analyses only on the study participants who had undergone colonoscopy or smoked before baseline (i.e., I_B = yes or S_B = yes) to eliminate any baseline imbalance.

The carrier status (i.e., G) distinguished carriers, who carried any deleterious mutations in the MMR or EPCAM genes, from non-carriers, who did not carry their families’ deleterious mutations in the MMR or EPCAM genes.

The receiver status at follow-up (i.e., R_F) distinguished 392 receivers, who had received their genetic testing results before follow-up, from 115 non-receivers, who had either received their results after follow-up (n=80) or never received them (n=35), among the 507 study participants.

The receiver status at follow-up (i.e., receiver/non-receiver) only allowed for the comparison of those who had received their results versus those who had not, which was not a pre- and post-intervention study design. To address this shortcoming, we conducted a subgroup analysis that directly compared 43 receivers, who had received their results between baseline and follow-up, and 80 non-receivers at follow-up, who had received their results after follow-up, in a pre- and post-intervention study design.

Previous cancer diagnosis at follow-up (i.e., H_F) distinguished those who had been diagnosed with some non-colorectal cancer cancer before follow-up from those who had not. We adjusted for this factor and interacted it with the carrier and receiver statuses at follow-up, following the observations of another study reporting that previous cancer diagnosis affected individual health behaviours differentially among receiver/non-receiver carrier/non-carrier subgroups (32). None of the study participants had had colorectal cancer at follow-up, since we selected for individuals with no previous diagnosis of colorectal cancer at follow-up. We also conducted subgroup analyses only on the study participants with no previous cancer diagnosis at follow-up (i.e., H_F = no) to eliminate any endogeneity associated with the variable.

We controlled for the study participants’ age at follow-up (i.e., years), sex (i.e., female/male), marital status (i.e., married or common-law/single), education (i.e., less than high school/vocational, training, or high school/college, university, or more), and degree of relatedness to their family probands (i.e., self or identical twin/first/second/third/higher degree)—factors previously identified to affect individual health behaviours that modulate the risk of cancer (32,33,34,35). The degree of relatedness to family probands also addressed potential heterogeneity in the study participants’ awareness of colorectal cancer from their family relationships.

All statistical tasks were performed with the Stata (version 12.1) software (36). The estimation results on the carrier and receiver statuses and previous cancer diagnosis at follow-up and their interaction terms were linearly combined to compare the receivers and non-receivers in the various carrier/non-carrier cancer/no-cancer subgroups to test the hypotheses from the previous section.

RESULTS

Descriptive Statistics

Table 2 reports the descriptive statistics on the study participants and non-participants. The study participants were, on average, in their mid-forties at baseline, more often female, and predominantly married or in a common-law relationship and had high-school education or more. Before baseline, 71% and 47% of the study participants had ever undergone colonoscopy or smoked, respectively, while 47% and 17% had undergone colonoscopy within two years or were currently smoking, respectively. By baseline, 13% had been diagnosed with some non-colorectal cancer cancer. Compared to the 507 study participants, the 1,246 non-participants were significantly older, more often male, more often single, with lower levels of education, more distant relatives to their family probands, and less likely to have undergone colonoscopy.

Table 2.

Descriptive statistics

	Study participants		Study non- participants		Participants vs. Non-participants	Receivers at follow-up				Non-receivers at follow-up				Non-/Receivers vs. Non-/Carriers
						Carriers		Non-carriers		Carriers		Non-carriers
	n	%/M(SD)	n	%/M(SD)	p-value	n	%/M(SD)	n	%/M(SD)	n	%/M(SD)	n	%/M(SD)	p-value
Total n	507	100%	1,246	100%		171	44%	221	56%	45	39%	70	61%
Socio-demographic factors
Age (years)
At baseline	507	43.7(14.5)	1,232	48.0(16.3)	<0.001***	171	41.5(14.1)	221	46.8(14.3)	45	37.8(13.3)	70	43.0(15.2)	<0.001***
At follow-up	500	48.9(14.5)	925	52.6(15.6)	<0.001***	165	46.9(14.0)	220	52.1(14.2)	45	42.9(13.2)	70	47.7(15.5)	<0.001***
Sex
Female (vs. male)	507	61%	1,246	56%	0.049*	171	66%	221	60%	45	51%	70	56%	0.206
Marital status
Married or common-law (vs. single)	505	74%	1,240	69%	0.043*	170	70%	220	77%	45	73%	70	76%	0.486
Education
Less than high school	497	31%	1,224	37%	0.046*	168	26%	215	34%	45	31%	69	33%	0.421
High, vocational, or training school		43%		39%			45%		43%		49%		36%
College, university, or more		26%		24%			29%		23%		20%		30%
Colorectal cancer awareness
Degree of relatedness to family probands
Self or identical twin	499	4%		3%	0.005*	171	8%	217	4%	45	0%	66	0%	0.330
1st degree		23%		25%			20%		24%		29%		23%
2nd degree		27%		33%			27%		28%		22%		29%
3rd degree		16%		17%			16%		15%		16%		18%
Higher degree		29%		22%			28%		29%		33%		30%
Health behaviours
Colonoscopy use (vs. no use)
Before baseline	507	71%	1,229	59%	<0.001***	171	77%	221	76%	45	53%	70	53%	<0.001***
Within two years of baseline	367	47%	908	35%	<0.001***	129	60%	157	46%	34	35%	47	21%	<0.001***
Between baseline & follow-up	491	68%	832	52%	<0.001***	161	94%	217	58%	45	62%	68	43%	<0.001***
Within two years of follow-up	449	54%	779	40%	<0.001***	156	91%	197	35%	35	37%	61	30%	<0.001***
Smoking (vs. no smoking)
Before baseline	506	47%	1,240	47%	0.507	170	49%	221	49%	45	53%	70	34%	0.115
At baseline	506	17%	1,232	16%	0.354	170	18%	221	17%	45	20%	70	9%	0.259
Between baseline & follow-up	454	21%	385	21%	0.483	144	23%	200	22%	44	25%	66	12%	0.264
At follow-up	454	17%	384	17%	0.474	144	19%	200	18%	44	20%	66	5%	0.042*
Health outcome
Previous cancer diagnosis (vs. no diagnosis)
At baseline	507	13%	1,246	12%	0.379	171	19%	221	12%	45	9%	70	7%	0.032*
At follow-up	507	19%	1,246	17%	0.329	171	26%	221	15%	45	20%	70	11%	0.022*

n, sample size. M, mean. SD, standard deviation. vs., versus. Statistical comparisons between the study participants and non-participants were made, using the Fisher's exact test for binomial variables, chi-squared test for categorical variables, and Mann-Whitney U test for continuous variables. Statistical comparisons among the four receiver/non-receiver carrier/non-carrier subgroups were made, using chi-squared test for binomial and categorical variables and Mann-Whitney U test for continuous variables.

Significance:

^***for 0.001;

^*for 0.05.

The study participants were stratified by the carrier and receiver statuses. The receivers were older than the non-receivers, suggesting that the receivers enrolled in the ACCFR earlier than the non-receivers. The carriers were younger than the non-carriers, which was expected, since identifying individuals with no previous diagnosis of colorectal cancer leads to selection for carriers who are younger than non-carriers. At baseline, the receivers had higher rates of colonoscopy use, compared to the non-receivers, likely because the receivers were older than the non-receivers and already under colon screening. Indeed, aging is a good predictor of colonoscopy use (37). The carriers had higher rates of previous diagnosis of non-colorectal cancer cancer, compared to the non-carriers, which was expected, since the carriers’ genetic mutations predisposed them to colorectal cancer, as well as other cancers (8). No other significant differences or indications of systemic differences were observed among the receiver/non-receiver carrier/non-carrier subgroups.

Estimation Results

Table 3 reports the estimation results of the logit regression models on the likelihood of undergoing colonoscopy or smoking between baseline and follow-up. Odds ratios (ORs) greater (or less) than one indicate higher (or lower) probabilities for undergoing colonoscopy or smoking versus not undergoing colonoscopy or smoking, respectively, between baseline and follow-up.

Table 3.

Estimation results of the logit regression models on colonoscopy use and smoking between baseline and follow-up

	Colonoscopy use between baseline and follow-up (vs. no use)				Smoking between baseline and follow-up (vs. no smoking)
Observations	473				442
Pseudo R2	0.288				0.449
Area under ROC curve	0.842				0.917
Somers’ D	0.566				0.794
Brier score	0.157				0.108
	OR	p-value		lrtest	OR	p-value		lrtest
Carrier status × Receiver status at follow-up × Previous cancer diagnosis at follow-up
Carrier (vs. non-carrier)	2.358	0.089		<0.001***	2.226	0.302		0.031*
Receiver (vs. non-receiver)	1.213	0.587			4.147	0.037*
Previous cancer diagnosis (vs. no diagnosis)	0.640	0.638			27.499	0.120
Carrier receiver (vs. non-carrier non-receiver)	10.822	0.001***			0.193	0.067
Carrier with previous cancer diagnosis (vs. non-carrier with no diagnosis)	2.001	0.604			0.084	0.344
Receiver with previous cancer diagnosis (vs. non-receiver with no diagnosis)	0.579	0.600			0.006	0.026*
Carrier receiver with previous cancer diagnosis (vs. non-carrier non-receiver with no diagnosis)	0.198	0.309			314.316	0.043*
Linear combination
Receiver carrier (vs. non-receiver carrier) with no previous cancer diagnosis	13.124	<0.001***			0.802	0.702
Receiver carrier (vs. non-receiver carrier) with previous cancer diagnosis	1.502	0.698			1.511	0.800
Receiver non-carrier (vs. non-receiver non-carrier) with no previous cancer diagnosis	1.213	0.587			4.147	0.037*
Receiver non-carrier (vs. non-receiver non-carrier) with previous cancer diagnosis	0.702	0.717			0.025	0.089
Socio-demographic factors
Age at follow-up
Age (years)	1.132	0.034*		0.079	0.852	0.060		<0.001***
Age squared	0.999	0.049*			1.001	0.262
Female (vs. male)	2.489	<0.001***			1.213	0.563
Married or common-law (vs. single)	1.100	0.759			0.397	0.020*
Education (vs. less than high school)
High, vocational, or training school	1.503	0.176		0.380	1.118	0.764		0.010**
College, university, or more	1.160	0.655			0.321	0.019*
Colorectal cancer awareness
Degree of relatedness to family probands (vs. self or identical twin)
1st degree	0.721	0.700		0.020*	0.808	0.820		0.776
2nd degree	0.460	0.356			0.753	0.754
3rd degree	0.400	0.286			0.438	0.389
Higher degree	0.245	0.091			0.788	0.794
Baseline health behaviours
Colonoscopy use before baseline (vs. no use)	4.414	<0.001***
Smoking before baseline (vs. no smoking)					210.723	<0.001***
Constant	0.015	0.014*			0.866	0.948

OR, odds ratio. ROC, receiver operating characteristic. lrtest, likelihood ratio test, using chi-squared statistics. vs., versus.

Significance:

^***for 0.001;

^**for 0.01;

^*for 0.05.

There were significant associations between the likelihood of undergoing colonoscopy between baseline and follow-up and the single and interaction terms of the carrier and receiver statuses and previous cancer diagnosis at follow-up. The study participants who discovered that they carried a genetic mutation predisposing to colorectal cancer (i.e., receiver carriers) were more likely to undergo colonoscopy, compared to those who were also carriers but had not received their results (i.e., non-receiver carriers). However, this was only true for the study participants with no previous cancer diagnosis (OR = 13.124, p-value < 0.001) and not for those with previous cancer diagnosis (OR = 1.502, p-value = 0.698). The study participants who discovered that they did not carry a genetic mutation predisposing to colorectal cancer (i.e., receiver non-carriers) were not different from those who were also non-carriers but had not received their results (i.e., non-receiver non-carriers) in their colonoscopy use (OR = 1.213, p-value = 0.587 for no previous cancer diagnosis; OR = 0.702, p-value = 0.717 for previous cancer diagnosis).

There were also significant associations between the likelihood of smoking between baseline and follow-up and the single and interaction terms of the carrier and receiver statuses and previous cancer diagnosis at follow-up. The study participants who discovered that they did not carry a genetic mutation predisposing to colorectal cancer (i.e., receiver non-carriers) were more likely to smoke, compared to those who were also non-carriers but had not received their results (i.e., non-receiver non-carriers). This was because the receiver non-carriers were less likely to quit smoking, and not because the receiver non-carriers were more likely to take up smoking, compared to the non-receiver non-carriers (see Supplementary Table S1). However, this was only true for the study participants with no previous cancer diagnosis (OR = 4.147, p-value = 0.037) and not for those with previous cancer diagnosis (OR = 0.025, p-value = 0.089). The study participants who discovered that they carried a genetic mutation predisposing to colorectal cancer (i.e., receiver carriers) were not different from those who were also carriers but had not received their results (i.e., non-receiver carriers) in their smoking behaviour (OR = 0.802, p-value = 0.702 for no previous cancer diagnosis; OR = 1.511, p-value = 0.800 for previous cancer diagnosis).

The ORs on the other explanatory variables were consistent with expectations. For colonoscopy, the age terms suggested increasing colonoscopy use until 57 years of age and decreasing colonoscopy use thereafter. Being female versus male was associated with a higher likelihood of undergoing colonoscopy. Closer relatives to family probands had a higher likelihood of undergoing colonoscopy, compared to more distant relatives. For smoking, the age terms suggested decreasing smoking with age. Being married or in a common-law relationship versus being single was associated with a lower likelihood of smoking. Higher education was associated with a lower likelihood of smoking. For both colonoscopy and smoking, each behaviour at baseline was a significant and positive predictor of itself between baseline and follow-up. The area under the receiver operating characteristic (ROC) curve, Somers’ D_xy rank correlation, and Brier’s score indicated good predictive abilities of the models.

Supplementary Table S2 and Table S3 report the results of the subgroup analyses, estimated only on the study participants who had undergone colonoscopy or smoked before baseline, respectively. Supplementary Table S4 reports the results of the subgroup analyses, estimated only on the study participants with no previous cancer diagnosis at follow-up. The results presented in Supplementary Table S2, Table S3, and Table S4 were in agreement with those presented in Table 3, although the results in Supplementary Table S2, Table S3, and Table S4 were lacking power, due to the smaller sample sizes.

Table 4 reports the estimation results of the logit regression models on the likelihood of undergoing colonoscopy within two years of follow-up or smoking at follow-up. Because some of the interaction terms involving previous cancer diagnosis at follow-up did not have any observations and could not be estimated, these models were estimated on the study participants with no previous cancer diagnosis at follow-up.

Table 4.

Estimation results of the logit regression models on recent colonoscopy and current smoking

	Colonoscopy use within two years of follow-up (vs. no use)				Smoking at follow-up (vs. no smoking)
Observations	248				362
Pseudo R2	0.363				0.569
Area under ROC curve	0.872				0.943
Somers’ D	0.567				0.832
Brier score	0.185				0.0618
	OR	p-value		lrtest	OR	p-value		lrtest
Carrier status x Receiver status at follow-up
Carrier (vs. non-carrier)	0.749	0.706		<0.001***	5.450	0.135		0.120
Receiver (vs. non-receiver)	0.903	0.847			8.549	0.030*
Receiver carrier (vs. non-receiver non-carrier)	59.740	<0.001***			0.117	0.365
Linear combination
Receiver carrier (vs. non-receiver carrier)	53.928	<0.001***			1.002	0.998
Receiver non-carrier (vs. non-receiver non-carrier)	0.903	0.847			8.549	0.030*
Socio-demographic factors
Age at follow-up
Age (years)	1.156	0.117		0.002**	1.130	0.365		0.102
Age squared	0.999	0.301			0.998	0.225
Female (vs. male)	1.676	0.139			1.028	0.954
Married or common-law (vs. single)	0.857	0.747			0.756	0.603
Education (vs. less than high school)
High, vocational, or training school	2.152	0.066		0.105	0.463	0.189		0.183
College, university, or more	1.038	0.938			0.280	0.078
Colorectal cancer awareness
Degree of relatedness to family probands (vs. self or identical twin)
1st degree	1.103	0.915		0.740	2.066	0.699		0.852
2nd degree	1.092	0.922			2.321	0.648
3rd degree	0.597	0.601			1.294	0.893
Higher degree	0.709	0.704			1.342	0.874
Baseline health behaviours
Colonoscopy use within two years of baseline (vs. no use)	1.624	0.202
Smoking at baseline (vs. no smoking)					97.021	<0.001***
Constant	0.002	0.007**			0.001	0.076

OR, odds ratio. ROC, receiver operating characteristic. lrtest, likelihood ratio test, using chi-squared statistics. vs., versus.

Significance:

^***for 0.001;

^**for 0.01;

^*for 0.05.

The results presented in Table 4 were in agreement with those presented in Table 3, although the results in Table 4 were lacking power, due to the smaller sample sizes.

Table 5 reports the estimation results of the logit regression model on the likelihood of undergoing colonoscopy between baseline and follow-up, in a subgroup analysis that directly compared the 43 receivers, who had received their results between baseline and follow-up, and the 80 non-receivers at follow-up, who had received their results after follow-up, in a pre- and post-intervention study design. Because some of the interaction terms involving previous cancer diagnosis at follow-up did not have any observations and could not be estimated, this model was estimated on the study participants with no previous cancer diagnosis at follow-up. Because none of the 43 and 80 study participants were first-degree relatives to their family probands, the degree of relatedness to family probands variable had four, instead of five, categories. For smoking, because the 43 receivers, who had received their results between baseline and follow-up, were lacking information on smoking at follow-up and automatically omitted from the estimation, the model could not be estimated.

Table 5.

Estimation results of the logit regression model on colonoscopy use between baseline and follow-up on the pre- and post-intervention study design subgroup

	Colonoscopy use between baseline and follow-up (vs. no use)
Observations	94
Pseudo R2	0.275
Area under ROC curve	0.829
Somers’ D	0.653
Brier score	0.156
	OR	p-value		lrtest
Carrier status × Receiver status at follow-up
Carrier (vs. non-carrier)	2.708	0.121		0.011*
Receiver (vs. non-receiver)	0.849	0.835
Carrier receiver (vs. non-carrier non-receiver)	7.208	0.136
Linear combination
Receiver carrier (vs. non-receiver carrier)	6.121	0.088
Receiver non-carrier (vs. non-receiver non-carrier)	0.849	0.835
Socio-demographic factors
Age at follow-up
Age (years)	1.078	0.533		0.482
Age squared	0.999	0.684
Female (vs. male)	3.577	0.035*
Married or common-law (vs. single)	1.556	0.556
Education (vs. less than high school)
High, vocational, or training school	1.450	0.589		0.571
College, university, or more	2.200	0.297
Colorectal cancer awareness
Degree of relatedness to family probands (vs. self or identical twin)
2nd degree	0.581	0.465		0.096
3rd degree	0.429	0.304
Higher degree	0.153	0.021*
Baseline health behaviours
Colonoscopy use before baseline (vs. No use)	5.070	0.007**
Constant	0.020	0.209

OR, odds ratio. ROC, receiver operating characteristic. lrtest, likelihood ratio test, using chi-squared statistics. vs., versus.

Significance:

^**for 0.01;

^*for 0.05.

The results presented in Table 5 were in agreement with those presented in Table 3, although the results in Table 5 were lacking power, due to the smaller sample size. For example, the carriers who had received their results between baseline and follow-up (i.e., receiver carriers) were more likely to undergo colonoscopy, compared to the carriers who had not received their results until after follow-up (i.e., non-receiver carriers) (OR = 6.121, p-value = 0.088).

DISCUSSION

We found strong evidence of a higher likelihood of undergoing colonoscopy for individuals with a family history of Lynch syndrome, but no previous cancer diagnosis, upon learning that they were carriers of a genetic mutation predisposing to colorectal cancer. There was also strong evidence of a lower likelihood of quitting smoking for individuals with a family history of Lynch syndrome, but no previous cancer diagnosis, upon learning that they were non-carriers of a genetic mutation predisposing to colorectal cancer. These empirical findings were consistent with the hypotheses generated from our simple health economics model. They also agreed well with previous studies that reported substitutive effects between genetic endowments and personal health behaviours in economic (38,39,40) and clinical (16,17,18,19,20,21,22,23) literature. They were also congruent with a previous study that reported modulating effects of previous cancer diagnosis (32). The empirical findings were robust to factors analyzed in subsequent and subgroup analyses, involving the recent colonoscopy use and current smoking measures and the pre- and post-intervention study design.

However, no empirical evidence was found to support the hypotheses that individuals with a family history of colorectal cancer are less likely to undergo colonoscopy upon learning their non-carrier status and also more likely to quit smoking upon learning their carrier status. These discrepancies may imply the receivers’ misunderstanding of their genetic status and/or difficulty in accepting the new information. Indeed, some of the previous studies also found non-carriers who continued to undergo colonoscopy (18) and identified the family history of colorectal cancer, as opposed to new information obtained through predictive genetic testing, as the most significant attribute to one’s perceived risk (41). Further, a recent study using the ACCFR data identified individuals from colorectal cancer-causing mutation-carrying families who perceived their risk of having colorectal cancer as high and did not receive or refused genetic testing results (28). A lack of guidance on appropriate colon screening behaviours may have been another factor, and further research on the screening advice given to receivers by their physicians is required. The discrepancies may also imply the receivers’ difficulty in overcoming an addiction and/or understanding that smoking is associated with high risk of colorectal cancer (13,14). Alternatively, the lack of significant evidence may reflect the fact that the predicted change from “use” to “no-use” would not be captured in our data if the study participants who received their genetic testing results between baseline and follow-up had undergone colonoscopy or smoked after baseline but before receiving their results.

Our study had many strengths. To the best of our knowledge, it was the first to investigate the impact of receiving predictive colorectal cancer genetic information on individual smoking behaviour and offered the longest follow-up duration of five years among the studies of its kind on colorectal cancer (16,17,18,19,20,21,22,23). However, there were several limitations.

Of the 1,753 individuals in the ACCFR who were potentially eligible for inclusion in our study, 1,246 had insufficient information on whether or when their genetic testing results had been disclosed to them and were excluded from the analysis. Our descriptive statistics identified significant differences in age, sex, marital status, education, degree of relatedness to family probands, and colonoscopy use between the study participants and non-participants. Being female and receiving family support have been shown to be positively associated with individual health behaviours that modulate the risk of colorectal cancer (32,37). Consistent with these observations, the study non-participants, who were more often male and more distant relatives to their probands, compared to the study participants, were less likely to have undergone colonoscopy. Therefore, the findings of our study might have been biased towards predicting healthier behaviours than it would have, had it included the study non-participants from the study population. In other words, the higher likelihood of undergoing colonoscopy for individuals upon learning that they were carriers and the lower likelihood of quitting smoking for individuals upon learning that they were non-carriers might in fact be lower in a broader population.

Because the ACCFR registrants did not choose to obtain genetic testing, the availability of their results was free of potential selection bias associated with the choice to undergo genetic testing. While there was potential selection bias associated with the choice to receive genetic testing results, our descriptive statistics found no indications of systemic differences between the receivers and non-receivers other than age, which was adjusted for in all our regression models. Further, the majority of the non-receivers at follow-up (i.e., 80 out of 115) did receive their results after follow-up, suggesting against inherent differences between the receivers and non-receivers in their choice to receive genetic testing results. Rather, the receiver status at follow-up was an artificial distinction, made to take advantage of the differential timing in the receipt of the results. Nevertheless, this being an observational study, there remain concerns that the receiver/non-receiver carrier/non-carrier subgroups were inherently different from each other on some unmeasured characteristics. Specifically, the lower likelihood of quitting smoking for individuals upon learning that they were non-carriers was driven by the particularly low rate of smoking in the non-receiver non-carriers. Our study must be replicated on different populations, to test not only the generalizability of our study findings but also their robustness.

For the majority of the receivers (i.e., 343 out of 392), the exact dates of their receipt were unavailable. As some of these receivers had likely received their results before baseline, baseline for the receivers did not necessarily represent “pre-intervention”. Therefore, the receiver status at follow-up only allowed for the comparison of those who had received their results versus those who had not, which was not a pre- and post-intervention study design. Assuming that behavioural change from this new information generally occurs shortly after its receipt, we took a conservative approach by including these individuals in our empirical analysis and still discovered significant effects. Further, our analysis on a pre- and post-intervention study design subgroup found consistent results, although the results were lacking power, due to the smaller sample sizes. Future studies may benefit from larger sample sizes.

In conclusion, our empirical analysis identified intended and unintended consequences of predictive genetic testing for colorectal cancer, suggesting that the impact of personalized medicine on disease prevention is more intricate than generally expected. The exact consequences of providing individuals with new information about their genetic disease risk will likely differ from one disease to another, with variations in the efficacy of the intervention, penetrance of the genetics, effectiveness of the preventive measures, and individual attitudes towards risk. Therefore, our research findings highlight the need for a fulsome assessment on the benefits and costs of personalized preventive interventions from both the clinical and societal perspectives that take account of individual responses to the resulting new information.