Double‐Edged Genetic Swords and Immunity: Lesson from CCR5 and Beyond
Was Shakespeare, that keen observer of human behavior, also an insightful infectious disease epidemiologist? Had he been prompted to pen these thoughts because he had astutely observed that some individuals resisted acquiring some infections or did poorly once infected compared with others? Validating Shakespeare’s crystal ball, 250 years later, it was first postulated that these fortresses are genetic traits, with J. B. S. Haldane and A. C. Allison suggesting that malaria was an evolutionary force that selected for malaria‐resistant genes. However, there is a tradeoff. As illustrated by the results reported by Murphy and colleagues in this issue of the Journal and the vignettes described below, it has become apparent that these genetic variants are oftentimes akin to a double‐edged sword, serving as a fortress against one infection while conferring susceptibility to another. For example, genetic traits that result in hemoglobin and/or red blood cell disorders (eg, sickle cell disease and thalessemia) protect against malaria. The African‐specific allele that results in the null state for Duffy antigen receptor for chemokines (DARC) on erythrocytes protects against Plasmodium vivax malaria. However, the role of DARC null state in infectious diseases is likely to be much more complex, because it may correlate with a blunted inflammatory response to endotoxins, serve as a genetic basis for the ethnic leukopenia that is observed commonly in persons of African ancestry, increase the risk of acquiring human immunodeficiency virus (HIV) infection, and confer a survival advantage to leukopenic HIV‐positive African Americans.
Murphy and colleagues now highlight another genetic tradeoff: the null state of CC chemokine receptor 5 (CCR5) is associated with early symptom development and more pronounced clinical manifestations after infection with West Nile virus (WNV) , whereas this same genetic state is known to confer strong protection against risk of acquiring HIV infection. The CCR5 null state, which is due to homozygosity for the European‐specific 32 base pair (bp) coding deletion mutation (Δ32), propelled the HIV field forward in the mid‐1990s, spawned an explosion of studies that explored the association of CCR5Δ32/Δ32 with a myriad of infectious and noninfectious diseases, and led to the development of CCR5 blockers for the treatment of HIV disease.
In retrospect, defining the link between CCR5 surface expression and HIV pathogenesis appears to be Act I of a 3‐act Shakespearean play on the role of CCR5. Act II is punctuated by scenes that reveal the double‐edged nature of the phenotypes associated with the possession of the CCR5Δ32/Δ32 genotype. From a historical perspective, it is noteworthy that the Murphy laboratory has played a major role in both acts thus far. In Act I, his laboratory was among the first to clone and functionally characterize CCR5 and, along with the Berger laboratory, demonstrate that CCR5 is the major coreceptor required for cell entry of HIV‐1. In Act II, although CCR5‐null mice were found to have immune perturbations following inflammatory challenges, the plot really heated up when the Murphy laboratory challenged these mice with WNV, a mosquito‐borne neurotropic flavivirus. From this point onward, the story resembles Macbeth.
Discussion
Our results demonstrate that the probability of emergent drug resistance decreased steadily during 1996–2004. Incomplete adherence and nonboosted PI–based or NNRTI‐based antiretroviral regimens were associated with a greater probability of the development of drug resistance. In contrast, boosted PI–based regimens were significantly associated with a lower emergence of resistance, even after adjustment for pVL and CD4 cell count. Of note, the latter remained the case at all levels of adherence.
Suboptimal adherence levels (80% to <95%) were associated with the highest risk of resistance in any drug category (hazard ratio [HR], 4.15; ) or in multiple resistance categories (HR, 6.99; ) (survival analysis data not shown). Recent studies have also shown that different ART classes have unique adherence‐resistance relationships. Bangsberg et al., gathering data from several studies, demonstrated that, with regimens containing nonboosted PIs, most drug resistance occurred in patients with adherence levels between 70% and 80%. The data obtained here show that boosted PI–based regimens are associated with relatively low levels of resistance development across all adherence strata, consistent with previous observations that boosted PI–based regimens may have a more “forgiving” profile in terms of virological suppression. Also, it is interesting to note that we observed a 2‐fold increase in the risk of resistance in patients with a history of injection drug use; this contrasts with an only slightly increased risk noted previously. The reasons for this difference are not clear.
There are several features of the present study that should be highlighted. First, this study was based on a large sample of patients within a provincewide treatment program, in which all individuals had free access to medical attention, combination ART, and laboratory monitoring. We are confident, therefore, that our results are not influenced by access to therapy, a factor that has often compromised the interpretation of similar population‐ and cohort‐based studies. Second, the very simple methodology of logistic regression was sufficient to show the simultaneous effects of adherence, pVL, and antiretroviral regimen on the development of drug resistance. Third, this study was based on individuals who were initially naive to ART, ensuring that our results were not confounded by previous therapy use.
There are some important potential limitations in our study. First, study participants who had samples with a pVL <1000 copies/mL were assumed to have no drug‐resistance mutations. To assess whether this introduced a possible source of bias, we conducted a sensitivity analysis excluding these individuals. This analysis showed that this assumption did not bias our results. Second, pretherapy genotypes were not available for the assessment of transmitted resistance in all study participants. We conducted a sensitivity analysis in the subset of individuals who had pretherapy genotypes without transmitted resistance and found that our original findings still held. Third, we used pharmacy‐refill compliance as a surrogate for adherence; however, this measure of adherence has been found to be independently associated with HIV suppression and survival among HIV‐infected individuals enrolled in the BC‐CfE DTP.
Increased Resilience to the Development of Drug Resistance with Modern Boosted Protease Inhibitor. Part 5
Of particular interest is the relationship between emergence of resistance, pVL, and adherence stratified by regimen type. The dependence of resistance selection on baseline pVL and patient adherence was markedly decreased for boosted PI–based regimens. The estimated probability of resistance for the worst adherence‐pVL stratum for boosted PI–based regimens was equal to or lower than that observed for any adherence stratum for nonboosted PI–based regimens and was very similar to that of NNRTI‐based regimens for adherence levels <80%. For those patients with a pVL in the <5 log10 copies/mL range, the risk of the development of drug resistance varied greatly depending on the first regimen. Sensitivity analyses.In the central analysis, samples with a pVL <1000 copies/mL were not genotyped and were assumed to not carry resistance mutations. We addressed the impact of this assumption by conducting a sensitivity analysis, eliminating those individuals with viral suppression (pVL < 1000 copies/mL) during the entire follow‐up period ( [23%]). An explanatory logistic regression model was developed for identifying which patient characteristics were the most influential in the development of drug resistance during antiretroviral treatment. The results from the new univariate analyses were consistent with the previous ones, and the multivariate analysis also yielded results similar to those presented before. Based on the multivariate model, we also observed no difference in the odds of the development of key resistance mutations between nonboosted PI–based regimens (reference group) and NNRTI‐based regimens (OR, 1.27 [95% CI, 0.95–1.68]) but greatly reduced odds for boosted PI–based regimens (OR, 0.36 [95% CI, 0.24–0.54]). Similar results were observed regarding associations between resistance and adherence, pVL, CD4 cell count, start of therapy, and history of injection drug use. In addition, we observed a similar reduction in the risk of detecting resistance in those who started HAART during 1999–2001 (OR, 0.79 [95% CI, 0.60–1.05]) or 2002–2004 (OR, 0.43; 0.30–0.63]), compared with those who started HAART during 1996–1998 (reference group). To address the potential impact of transmitted resistance on resistance that develops during therapy, we conducted another sensitivity analysis examining only those 1426 individuals for whom pretherapy genotypes were available and excluding those who exhibited transmitted resistance, leaving a total of 1295 patients. As with the other sensitivity analysis, the results from the new univariate and multivariate analyses yielded similar results as the original. Based on the multivariate model, we observed no difference in the odds of the development of key resistance mutations between nonboosted PI–based regimens (reference group) and NNRTI‐based regimens (OR, 1.24 [95% CI, 0.87–1.77]) but greatly reduced odds for boosted PI–based regimens (OR, 0.44 [95% CI, 0.24–0.83]). Similar results to those of the original analysis were observed regarding associations between resistance and adherence, pVL, CD4 cell count, start of therapy, and history of injection drug use. Once again, we observed a similar reduction in the risk of detecting resistance in those who started HAART during 1999–2001 (OR, 1.01 [95% CI, 0.72–1.44]) or 2002–2004 (OR, 0.46 [95% CI, 0.28–0.79]), compared with those who started HAART during 1996–1998 (reference group).
To address the potential effects of wild‐type virus outgrowth in patients who ceased active therapy, we conducted another sensitivity analysis eliminating the 117 individuals who were not receiving any therapy at the time of resistance detection. Overall, we obtained results similar to those of the original analysis and of other sensitivity analyses. Of particular note, the effect of boosted PIs on the OR for resistance was even greater than that in the original analysis (OR, 0.37 [95% CI, 0.24–0.56]).
Increased Resilience to the Development of Drug Resistance with Modern Boosted Protease Inhibitor. Part 4
Demographic characteristics. Between August 1996 and November 2004, a total of 2350 antiretroviral‐naive participants (81.6% males) at least 18 years old started triple‐combination therapy in British Columbia and were eligible to participate in this study. Of these patients, 991 (42%) individuals initiated nonboosted PI–based regimens, 475 (20%) initiated boosted PI–based regimens, and 884 (38%) initiated NNRTI‐based regimens. The temporal change toward increasing prevalence of ritonavir‐boosted regimens in recent years reflects the changes in treatment guidelines over time. During a median of 4.8 years (IQR, 2.7–10.0) of follow‐up, a total of 6066 resistance tests were done, and resistance to at least one drug category developed in 650 (28%) patients. The development of drug resistance was associated with a higher pVL, history of injection drug use, NNRTI‐based regimens, starting therapy in 1996–1998, age, and higher adherence levels. Sex, AIDS diagnosis, and CD4 cell count were not significantly associated with the development of drug resistance.
Probability of drug‐resistance development. The univariate analysis for baseline characteristics showed that age, pVL, history of injection drug use, first regimen, year of first therapy, and adherence were associated with the development of drug resistance. The multivariate model predicted no difference in the odds of the development of key resistance mutations between nonboosted PI–based regimens (reference group) and NNRTI‐based HAART regimens (odds ratio [OR], 1.09 [95% confidence intervals {CI}, 0.84–1.42]) but greatly reduced odds for boosted PI–based regimens (OR, 0.42 [95% CI, 0.28–0.62]). A skewed, nonlinear relationship with adherence was confirmed, as was a strong association of resistance with increasing pVL. A weaker association with decreasing CD4 cell count, late start of therapy, and history of injection drug use was also observed. Of interest, after adjusting for other parameters, there was a reduction in the risk of detecting resistance in those who started HAART during 1999–2001 (OR, 0.83) or 2002–2004 (OR, 0.43), compared with those who started HAART during 1996–1998 (reference group).
There were no visible differences in the relationship when the data were stratified by CD4 cell count; however, when stratified by year of first therapy, the data indicated that individuals starting therapy during 1996–1998 presented the highest probabilities for the development of drug resistance, and this probability decreased linearly until 2002–2004. Furthermore, stratifying by pVL demonstrated that individuals in the baseline pVL <5 log10 copies/mL stratum had a considerably lower probability for the development of resistance than those with baseline pVL values 5 log10 copies/mL at equivalent levels of adherence.
Increased Resilience to the Development of Drug Resistance with Modern Boosted Protease Inhibitor. Part 3
Of the 2350 individuals included in this analysis, at least 1 available pretherapy genotype was available for 1426 (61%). Primary resistance was assessed using a standardized list of mutations suitable for transmitted resistance surveillance. From the earliest available pretherapy genotypes, 131 (9.2 %) showed some evidence of transmitted resistance. A sensitivity analysis was conducted that eliminated those individuals who were known to have transmitted resistance, leaving 1295 patients.
For each individual, the median number of genotypes that could possibly have been included in the analysis for which data was unavailable was 0 (interquartile range [IQR], 0–1) and the median number completed was 1 (IQR, 0–2). A total of 1433 (61.0%) of individuals had all possible genotypes completed.
Outcome measures and predictor variables.The primary outcome in this analysis was the emergence of drug resistance in any of the four resistance categories described previously (yes vs. no). The following baseline predictor variables were investigated: age, sex, CD4 cell count, pVL (log10 transformed), first regimen, AIDS diagnosis, history of injection drug use, year of first therapy, and adherence. Estimates of adherence to ART were based on medications actually dispensed, not prescribed. For this study, we limited our measure of adherence to the first year of therapy, estimated by dividing the number of months of medications dispensed by the number of months of follow‐up. This measure of adherence has been found to be independently associated with HIV suppression and survival among HIV‐infected persons enrolled in the BC‐CfE DTP. Adherence was categorized as 0% to <40%, 40% to <80%, 80% to <95% and 95%. Because pVL was measured over time starting in 1996, our baseline pVLs were obtained on the basis of the standard pVL assay, and our last measurements were obtained on the basis of the ultrasensitive pVL assay; thus, our upper and lower limits of pVLs ranged over time from 500 and to 50 and copies/mL. Therefore, our pVL measurements were recoded to range from 500 to copies/mL in order to standardize the viral load range over time. Statistical analyses.An exploratory logistic regression model was developed for identifying which patient characteristics were most influential in the development of drug resistance during ART. A backward stepwise technique was used in the selection of covariates. The area under the receiver operating characteristic curve was used to measure the model’s ability to discriminate between those in whom resistance developed versus those in whom it did not. Categorical variables were compared using the χ2 or Fisher’s exact test, and continuous variables were compared using the Wilcoxon rank‐sum test. For the purposes of analysis, we followed the intent‐to‐treat principle, with subjects retained in their initial treatment groups irrespective of whether participants subsequently switched to regimens that were available later. This approach provides a conservative estimate of the true treatment effect. All analyses were performed using SAS software (version 9.1.3, service pack 3).
Increased Resilience to the Development of Drug Resistance with Modern Boosted Protease Inhibitor. Part 2
HIV/AIDS drug distribution program.The British Columbia Centre for Excellence in HIV/AIDS (BC‐CfE) distributes antiretroviral agents at no cost to all eligible HIV‐infected individuals through its drug distribution program, the HIV/AIDS Drug Treatment Program (BC‐CfE DTP). This program has been described in detail elsewhere. ART is distributed according to the specific guidelines generated by the Therapeutic Guidelines Committee. The BC‐CfE’s guidelines have been regularly updated and remain consistent with those of the International AIDS Society–USA. The BC‐CfE DTP has received ethical approval from the University of British Columbia Ethics Review Committee at its St. Paul’s Hospital site.
Study participants. Eligible study participants were 18 years old and were naive to ART when they started HAART (consisting of 2 nucleosides/nucleotides plus either a nonboosted PI, an NNRTI, or a PI plus <800 mg of ritonavir [boosted PI]). Participants started treatment between 1 August 1996 and 30 November 2004 and were followed up until 30 November 2005 ( , with median follow‐up of 4.8 years and a total of 6066 tests). Participants must have had a CD4 cell count and pVL measurement within 6 months of the first antiretroviral start date. Study data from eligible participants were extracted from the BC‐CfE’s monitoring and evaluation system to form the HOMER (HAART Observational Medical Evaluation and Research) cohort. The characteristics of this study population have been extensively described elsewhere. Data collection.HIV‐positive individuals receiving ART in British Columbia are entered into an Oracle‐based monitoring and evaluation system that uses standardized indicators to prospectively track antiretroviral use and the clinical health status of these individuals. Physicians enrolling an HIV‐infected individual into the system must complete a drug request enrollment prescription form, which compiles information on the participant’s address, past HIV‐specific drug history, CD4 cell counts, pVL, current drug requests, and the enrolling physician. A qualified practitioner reviews all requests to verify that they follow the therapeutic guidelines outlined by the BC‐CfE. Approved prescriptions are renewed every 1 to 3 months. The BC‐CfE recommends that pVLs and CD4 cell counts be monitored at baseline, at 4 weeks after the start of ART, and every 3 months thereafter. pVLs are determined using the Roche Amplicor Monitor assay (Roche Diagnostics) by either the standard method or the ultrasensitive adaptation (since 1999). CD4 cell counts are measured by flow cytometry, followed by fluorescent monoclonal antibody analysis (Beckman Coulter).
Resistance testing was performed on stored pVL samples extracted manually or automatically using guanidinium‐based buffer, followed by ethanol washes. Protease (PR) and reverse‐transcriptase (RT) genes were amplified from plasma HIV‐1 RNA by nested RT polymerase chain reaction (PCR), as described elsewhere. PCR products were sequenced in both the 5′ and 3′ directions using an ABI automated sequencer, and a consensus sequence was generated. Results of the genotyping analysis are reported here as amino acid changes in the HIV PR and RT genes with respect to a wild‐type reference sequence (HIV HXB2; GenBank accession number K03455). Samples were assigned to 1 of 4 resistance categories on the basis of a modification of the International AIDS Society–USA table. Samples were considered to be resistant if they displayed 1 or more major resistance mutations in any of the following 4 categories: lamivudine (184I/V); any other nucleoside reverse‐transcriptase inhibitors (NRTIs; 41L, 62V, 65R, 67N, 69D or insertion, 70R, 74V, 75I, 151M, 210W, 215F/Y, or 219E/Q); any NNRTIs (100I, 103N, 106A/M, 108I, 181C/I, 188C/H/L, 190A/S, P225H, M230L, or 236L); and any PIs (30N, 46I/L, 48V, 50L/V, 54V/L/M, 82A/F/S/T, 84V, or 90M). Lamivudine resistance was analyzed as a separate category because of the very common appearance of this mutation and the lack of cross‐resistance conferred to other NRTIs. The percentage of samples that were obtained while individuals were being actively prescribed any ART were as follows: 82% for first lamivudine resistance, 78% for other NRTIs, 84% for NNRTIs, and 81% for PIs. Because genotyping does not yield consistently successful results for samples with low pVL, samples with a pVL <1000 copies/mL were not systematically genotyped and were assumed to have no drug‐resistance mutations. We conducted a sensitivity analysis to assess the impact of our assumption that samples with a pVL <1000 copies/mL harbored no drug‐resistance mutations. For the sensitivity analysis, we repeated the original analysis only for those individuals with at least 1 sample that had a pVL 1000 copies/mL. Resistance data from those who started therapy between August 1996 and September 1999 have been reported elsewhere.
Increased Resilience to the Development of Drug Resistance with Modern Boosted Protease Inhibitor
Highly active antiretroviral therapy (HAART) has led to a dramatic decrease in AIDS‐related comorbidity and mortality. However, the success of HAART can be compromised by the development of HIV drug resistance. Antiretroviral resistance is an independent risk factor for virologic failure in HIV‐1–infected populations. Drug‐resistance testing is now widely recommended in HIV therapy monitoring to detect the development of resistance to antiretrovirals and make appropriate regimen changes.
Several individual factors have been associated with the development of HIV‐1 drug resistance during HAART, including incomplete adherence to therapy, high baseline plasma viral load (pVL), and low CD4 cell count. However, there has been limited research on how the simultaneous presence of these factors and their interactions affect the development of antiretroviral resistance. Recent studies have shown that the relationship between adherence and resistance is complex. The accumulation of resistance mutations across all levels of adherence was greater in treatment‐naive individuals beginning HAART than in those who were treatment experienced. The heterogeneity in the relationship between adherence and resistance in antiretroviral‐exposed and ‐naive populations has been explored via computer simulations.
The relationship between adherence and resistance is also dependent on the individual drug classes used in combination therapy, with discrepancies between the adherence‐resistance relationships for nonnucleoside reverse‐transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs) being at least partially explained by differences in the replicative capacity of drug‐resistant versus wild‐type virus in the presence of clinically relevant drug levels for each drug, respectively. Recommendations for the optimal application of HAART have varied over time as therapies have evolved to become more convenient and tolerable. One particularly relevant change has been the widespread shift of international guidelines in favor of low‐dose ritonavir as a PI “boosting” agent when PI‐based HAART is used. The use of boosted PIs have led to improved virological suppression, as detailed in clinical trials and cohort studies, as well as improved clinical outcomes in cohort studies in observational settings. However, the impact of boosted PI–based regimens versus nonboosted PI–based and NNRTI‐based regimens on the development of HIV drug resistance mutations remains to be defined in a population‐based setting.
The objective of the present study was to characterize the probability of the development of drug resistance in drug‐naive individuals starting HAART in the modern era, adjusting for the simultaneous effects of adherence, pVL, and initial HAART regimen. We based our analysis on data from a large population‐based cohort of HIV‐1–infected antiretroviral‐naive adults initiating HAART in British Columbia, Canada, between 1 August 1996 and 30 November 2004.
Human T Lymphotropic Virus Type 1 Infection. Part 4
In the present study, conducted during a follow‐up period of 10‐years, HTLV‐1 infection was associated with a 3‐fold reduction in the risk of gastric cancer, suggesting that HTLV‐1 infection plays a role in reducing the risk for gastric cancer. We therefore looked at whether HTLV‐1 infection may reduce the risk of H. pylori infection. Isomoto et al. and Walker demonstrated that patients infected with HTLV‐1 had a low prevalence of H. pylori infection. Tachibana et al. showed that healthy carriers of HTLV‐1 exhibited significant diminution of delayed‐type hypersensitivity, suggesting the existence of subclinical immunosuppression even among healthy carriers of HTLV‐1. It is possible that, during long‐term infection with HTLV‐1, progression of immunosuppression provides a less suitable intragastric environment for H. pylori colonization and that the organism may gradually be eliminated from the stomach. Stuver et al. reported that significantly fewer HTLV‐1–positive patients had a past history of peptic ulcer, with odds ratios of 0.49 (95% CI, 0.27–0.89) and 0.81 (95% CI, 0.42–1.6) for male and female patients, respectively.
In this study, the rate of H. pylori positivity in the HTLV‐1–positive group was lower than that in HTLV‐1–negative group overall and for men and women; furthermore, the frequencies of gastric cancer among men, women, and both groups combined were greater among HTLV‐1–negative patients than among HTLV‐1–positive patients, although the differences were not significant. These findings suggest that HTLV‐1 infection may reduce the risk of H. pylori infection and proliferation. Among all men and HTLV‐1–positive women with gastric cancer, the number of patients with intestinal‐type carcinoma was greater than the number with diffuse‐type carcinoma. In contrast, among HTLV‐1–negative women, the number with diffuse‐type carcinoma was greater than the number with intestinal‐type carcinoma. The reason for the latter finding remains unclear.
Yamashita et al. determined the prevalence of H. pylori infection in healthy children from the Kyushu region in Japan. The prevalences of H. pylori seropositivity were 3% among children aged <1 year, 10% among those aged 1–4 years, 19% among those aged 5–9 years, 25% among those aged 10–14 years, and 29% among those aged 15–19 years. H. pylori infection is known to be acquired early during childhood. Infection frequently occurs before 10 years of age, and two‐thirds of individuals become infected before 7 years of age. Furthermore, family structure during early life (i.e., sibship size and birth order) is associated with a risk of future development gastric cancer among H. pylori–positive males. On the other hand, invasion by infected lymphocytes, which occurs via vertical infection (in 70% of cases), horizontal infection (in 20%), or transmission through blood (in 10%), is required for HTLV‐1 infection in vivo. A relatively high proportion of babies born to women positive for HTLV‐1 test positive for anti–HTLV‐1 antibody, although this antibody disappears within the first 9 months of life in many cases and by 2 years of age in nearly all cases. Among children with infection transmitted from their mother, there were no cases of seroconversion after the age of 3 years. These findings suggest that HTLV‐1 infection is frequently acquired before H. pylori infection. Early infection with HTLV‐1 may weaken gastric mucosal immune responses and thereby affect H. pylori infection and proliferation. In this study, we did not adjust for H. pylori infection, smoking, drinking habits, and salt intake, which could be related to the etiology of gastric cancer. Further studies of gastric cancer associated with HTLV‐1 seropositivity with adjustment for these factors are needed.
Human T Lymphotropic Virus Type 1 Infection. Part 3
The cumulative incidence of gastric cancer was 1.1% during the first 5 years of follow‐up (1994–1998) and 3.0% during the entire (i.e., 10‐year) follow‐up period (1999–2003) in the HTLV‐1–positive group, compared with 2.7% and 8.0%, respectively, in the HTLV‐1–negative group. The incidence of gastric cancer in the HTLV‐1–positive group was lower than that in the HTLV‐1–negative group. Among men, the cumulative incidence of gastric cancer was 1.8% during the first 5 years of follow‐up and 4.8% during the overall follow‐up period in the HTLV‐1–positive group, compared with 4.6% and 11.1%, respectively, in the HTLV‐1–negative group. Among women, the cumulative incidence of gastric cancer was 0.7% for the 5‐year period and 1.9% for the 10‐year period in the HTLV‐1–positive group, compared with 1.7% and 6.1%, respectively, in the HTLV‐1–negative group.
The cumulative survival rate during the first 5 years of follow‐up among the 49 patients with gastric cancer was 28.6% (4 of 14 patients) in the HTLV‐1–positive group, compared with 34.3% (12 of 35 patients) in the HTLV‐1–negative group.
We found that the incidence of gastric cancer in the HTLV‐1–positive group was lower than that in the HTLV‐1–negative group. Iwata et al. reported that the hazard ratio associated with HTLV‐1 infection for death from all causes, excluding ATL, was 1.77 (95% CI, 0.93–3.37) for males and 1.87 (95% CI, 1.12–3.12) for females, although analysis of cause‐specific mortality revealed a significantly increased risk for nonneoplastic disease in each group. Arisawa et al. reported that HTLV‐1‐seropositivity was associated with increased mortality from all causes, excluding ATL (risk ratio [RR], 1.3; 95% CI, 1.0–1.7), and that HTLV‐1 infection was not associated with an increased risk of any cancer other than ATL, colorectal cancer, liver cancer, and lung cancer but was associated with a reduced risk of gastric cancer (RR, 0.42; 95% CI, 0.17–0.99). In addition, Asou et al. and Kozuru et al. reported that HTLV‐1 infection was associated with an increased risk of various cancers.
There is increasing evidence to suggest that gastric infection with H. pylori is a risk factor for gastric cancer. Stored serum samples collected from individuals without gastric cancer were tested for IgG antibodies to H. pylori by ELISA. The mean time between serum collection and diagnosis of gastric carcinoma was 14.2 years. Of the 109 patients with confirmed gastric cancer, 84% had been infected previously with H. pylori, compared with 61% of matched control subjects (OR, 3.6; 95% CI, 1.8–7.3). The Eurogast Study Group demonstrated a significant correlation between the gastric cancer mortality rate and the prevalence of H. pylori seropositivity. It can be predicted that mortality from gastric cancer in a population with a 100% prevalence of H. pylori infection would be 6 times that in a population with a 0% prevalence. The World Health Organization and the International Agency for Research on Cancer consensus group stated in 1994 that there was sufficient epidemiologic and histologic evidence to classify H. pylori as a definite carcinogen. In 1998 Huang et al. reported results of a meta‐analysis of the relationship between H. pylori seropositivity and gastric cancer, Asaka et al. and Kikuchi et al. reported an association between H. pylori infection and the development of gastric cancer, and in 2001 Uemura et al. reported that gastric cancer developed in 4.7% of persons infected with H. pylori but in no uninfected persons during a mean follow‐up duration of 7.8 years. It has thus become clear that H. pylori infection is associated with the development of gastric cancer.
Human T Lymphotropic Virus Type 1 Infection. Part 2
Laboratory analysis. Serum HTLV‐1 antibody examination was performed using particle agglutination assays. The Serodia ATLA kit (Fujirebio) was used through April 1990, after which the Serodia HTLV‐1 kit (Fujirebio) was used.
Stored serum samples collected from all patients who developed gastric cancer underwent EIA (SRL) for detection of IgG antibodies to H. pylori. An IgG antibody titer of 10 U/mL was considered indicative of H. pylori. The samples were collected before the development of gastric cancer and the initiation of H. pylori eradication therapy.
A total of 296 of 994 individuals underwent a histologic examination, a 13C urea breath test, and, as described above, EIA for IgG antibodies to H. pylori. One hundred twenty (55 men and 65 women) of 497 examined patients were in the HTLV‐1–seropositive group, and 176 (71 men and 105 women) of 497 were in the HTLV‐1–seronegative group. H. pylori positivity was defined as a positive result of any of these assays.
Statistical analysis. The demographic characteristics of the study subjects were compared using the Student t test (for age and duration of follow‐up). The rate of H. pylori positivity was evaluated with the Fisher exact test. The cumulative incidence and survival rate for patients with gastric cancer were evaluated with the Kaplan‐Meier method and were compared using the log‐rank test. Odds ratios (ORs), their 95% confidence intervals (CIs), and statistical significance were computed. All analyses were performed with StatView, version 5.0.
Results
The rate of H. pylori positivity was 61.7% in the HTLV‐1–positive group, compared with 71.6% in the HTLV‐1–negative group. Among men, the rates were 56.4% in the HTLV‐1–positive group and 74.6% in the HTLV‐1–negative group, and among women, the rates were 66.2% and 69.5%, respectively.
There were 14 cases of gastric cancer (incidence, 2.8% [3.0 cases/year per 1000 population]) among HTLV‐1–positive patients and 35 cases (incidence, 7.0% [7.3 cases/year per 1000 population]) among HTLV‐1–negative control patients (OR, 0.38; 95% CI, 0.21–0.70). For men, there were 8 cases in the HTLV‐1–positive group and 19 cases in the HTLV‐1–negative group (OR, 0.39; 95% CI, 0.17–0.90), and for women, there were 6 and 16 cases, respectively (OR, 0.36; 95% CI, 0.14–0.91). The mean age at the time of gastric cancer onset was 72 years (range, 49–96 years) in the HTLV‐1–positive group, compared with 70 years (range, 42–77 years) in the HTLV‐1–negative group. In the HTLV‐1–positive group, there were 9 cases of intestinal‐type carcinoma, 2 cases of diffuse type, and 3 cases of unknown type, compared with 14 cases of intestinal‐type carcinoma, 15 cases of diffuse type, 1 case of mixed type, and 5 cases of unknown type in the HTLV‐1‐negative group. Eight (57.1%) of 14 patients in the HTLV‐1–positive group had been infected with H. pylori, compared with 27 (77.1%) of 35 patients in the HTLV‐1–negative group. The mean observation period (±SD) was years in the HTLV‐1–positive group and years in the HTLV‐1–negative group.