Sugars in diet and risk of cancer in the NIH-AARP Diet and Health Study.

Prospective epidemiologic data on the effects of different types of dietary sugars on cancer incidence have been limited. In this report, we investigated the association of total sugars, sucrose, fructose, added sugars, added sucrose and added fructose in the diet with risk of 24 malignancies. Participants (n = 435,674) aged 50-71 years from the NIH-AARP Diet and Health Study were followed for 7.2 years. The intake of individual sugars was assessed using a 124-item food frequency questionnaire (FFQ). Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) in multivariable models adjusted for confounding factors pertinent to individual cancers. We identified 29,099 cancer cases in men and 13,355 cases in women. In gender-combined analyses, added sugars were positively associated with risk of esophageal adenocarcinoma (HR(Q5 vs. Q1) : 1.62, 95% CI: 1.07-2.45; p(trend) = 0.01), added fructose was associated with risk of small intestine cancer (HR(Q5 vs. Q1) : 2.20, 95% CI: 1.16-4.16; p(trend) = 0.009) and all investigated sugars were associated with increased risk of pleural cancer. In women, all investigated sugars were inversely associated with ovarian cancer. We found no association between dietary sugars and risk of colorectal or any other major cancer. Measurement error in FFQ-reported dietary sugars may have limited our ability to obtain more conclusive findings. Statistically significant associations observed for the rare cancers are of interest and warrant further investigation.

Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations.

The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration-estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001--2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature.

Use of the predictive sugars biomarker to evaluate self-reported total sugars intake in the Observing Protein and Energy Nutrition (OPEN) study.

BACKGROUND: A predictive biomarker for intake of total sugars was recently developed under controlled conditions. We used this biomarker to assess measurement error (ME) structure in self-reported intake of total sugars in free-living individuals. METHODS: The Observing Protein and Energy Nutrition (OPEN) study involved 484 participants aged 40 to 69 years. Diet was assessed using two administrations of a food frequency questionnaire (FFQ) and two nonconsecutive 24-hour dietary recalls (24HDR). Two 24-hour urine samples checked for completeness were analyzed on sucrose and fructose. We applied the biomarker calibrated in a feeding study to OPEN data to assess the ME structure and the attenuation factors (AF) for intakes of absolute total sugars and sugars density for the FFQ and 24HDR. RESULTS: The AFs for absolute sugars were similar for a single FFQ and 24HDR, but attenuation decreased with repeated 24HDRs. For sugars density, the AFs for FFQ (men: 0.39; women: 0.33) were greater than for single 24HDR (men: 0.30; women: 0.24), and similar to two 24HDRs (men: 0.41; women: 0.35). The attenuation associated with both instruments was greater in women than in men. CONCLUSIONS: Both the FFQ and 24HDR were found to be biased; hence, incorporation of the sugars biomarker in calibration studies within the cohorts may be necessary to more reliably estimate associations of sugars and disease. IMPACT: In this article, we propose a new dietary reference instrument based on the recently defined class of predictive biomarkers. Using sugars biomarker, we quantify ME in the FFQ- and 24HDR-reported absolute total sugars and total sugars density.

No effect of meat, meat cooking preferences, meat mutagens or heme iron on lung cancer risk in the prostate, lung, colorectal and ovarian cancer screening trial.

Recent epidemiological studies have suggested that red and processed meat may increase the risk of lung cancer. Possible underlying mechanisms include mutagens produced during high-temperature cooking or preservation, or formed endogenously from heme iron in meat. We used data from 99,579 participants of both screened and nonscreened arms of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, aged 55-74 years, to investigate whether meat type, cooking method, doneness level, intake of specific meat mutagens 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo[4,5-f]quinoxaline] (DiMeIQx), 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) and benzo(a)pyrene (B(a)P)] and heme iron are associated with lung cancer. Participants' diet was assessed prospectively using a 124-item food frequency questionnaire and an additional meat-cooking module. Dietary data were used in conjunction with a database to estimate intake of MeIQx, DiMeIQx, PhIP, B(a)P and heme iron. After up to 8 years of follow-up, 782 incident lung cancer cases were ascertained. Lung cancer risk was not associated with the consumption of either red (men: HR(Q5 vs. Q1) = 1.11, 95% CI = 0.79-1.56, P(trend) = 0.42; women: HR(Q5 vs. Q1) = 1.30, 95% CI = 0.87-1.95, P(trend) = 0.65) or processed meat (men: HR(Q5 vs. Q11) = 1.12, 95% CI = 0.83-1.53, P(trend) = 0.22; women: HR(Q5 vs. Q1) = 0.98, 95% CI = 0.68-1.41, P(trend) = 0.32) in multivariable models. High-temperature cooking methods, level of meat doneness, meat mutagens and heme iron had no effect on lung cancer risk. In this population, we found no association between meat type, cooking method, doneness level or intake of specific meat mutagens or heme iron and lung cancer risk.

Gains in statistical power from using a dietary biomarker in combination with self-reported intake to strengthen the analysis of a diet-disease association: an example from CAREDS.

A major problem in detecting diet-disease associations in nutritional cohort studies is measurement error in self-reported intakes, which causes loss of statistical power. The authors propose using biomarkers correlated with dietary intake to strengthen analyses of diet-disease hypotheses and to increase statistical power. They consider combining self-reported intakes and biomarker levels using principal components or a sum of ranks and relating the combined measure to disease in conventional regression analyses. They illustrate their method in a study of the inverse association of dietary lutein plus zeaxanthin with nuclear cataracts, using serum lutein plus zeaxanthin as the biomarker, with data from the Carotenoids in Age-Related Eye Disease Study (United States, 2001-2004). This example demonstrates that the combined measure provides higher statistical significance than the dietary measure or the serum measure alone, and it potentially provides sample savings of 8%-53% over analysis with dietary intake alone and of 6%-48% over analysis with serum level alone, depending on the definition of the outcome variable and the choice of confounders entered into the regression model. The authors conclude that combining appropriate biomarkers with dietary data in a cohort can strengthen the investigation of diet-disease associations by increasing the statistical power to detect them.

Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies?

Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes. The authors propose using biomarkers known to be correlated with dietary intake, so as to strengthen analyses of diet-disease hypotheses. The authors consider combining self-reported intakes and biomarker levels using principal components, Howe's method, or a joint statistical test of effects in a bivariate model. They compared the statistical power of these methods with that of conventional univariate analyses of self-reported intake or of biomarker level. They used computer simulation of different disease risk models, with input parameters based on data from the literature on the relationship between lutein intake and age-related macular degeneration. The results showed that if the dietary effect on disease was fully mediated through the biomarker level, then the univariate analysis of the biomarker was the most powerful approach. However, combination methods, particularly principal components and Howe's method, were not greatly inferior in this situation, and were as good as, or better than, univariate biomarker analysis if mediation was only partial or non-existent. In some circumstances sample size requirements were reduced to 20-50% of those required for conventional analyses of self-reported intake. The authors conclude that (i) including biomarker data in addition to the usual dietary data in a cohort could greatly strengthen the investigation of diet-disease relationships, and (ii) when the extent of mediation through the biomarker is unknown, use of principal components or Howe's method appears a good strategy.

A prospective study of meat, cooking methods, meat mutagens, heme iron, and lung cancer risks.

BACKGROUND: Red and processed meat consumption may play a role in lung cancer pathogenesis because of these meats' fat and carcinogen content. OBJECTIVE: We prospectively investigated whether meat type, cooking method, doneness level, and intake of specific meat mutagens and heme iron are associated with lung carcinoma. DESIGN: Men (n = 278,380) and women (n = 189,596) from the National Institutes of Health-AARP Diet and Health Study with no history of cancer at baseline were monitored for 8 y. Diet was assessed with a 124-item food-frequency questionnaire. A meat-cooking module was used to estimate the intake of individual heterocyclic amines, benzo(a)pyrene, and heme iron. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs. RESULTS: In a comparison of quintiles 5 with 1 (Q5vsQ1), a high intake of red meat was associated with an increased risk of lung carcinoma in both men (HR(Q5vsQ1): 1.22; 95% CI: 1.09, 1.38; P for trend = 0.005) and women (HR(Q5vsQ1): 1.13; 95% CI: 0.97, 1.32; P for trend = 0.05). A high intake of processed meat increased the risk only in men (HR(Q5vsQ1): 1.23; 95% CI: 1.10, 1.37; P for trend = 0.003). In an analysis stratified by smoking status, we observed a tendency for an increased risk with red meat intake in never smoking men and women; however, the risks were not statistically significant. In a comparison of tertiles 3 and 1 (T3vsT1), the risk of lung carcinoma was associated with intake of well-/very-well-done meat (HR(T3vsT1): 1.20; 95% CI: 1.07, 1.35; P for trend = 0.002) and the intake of 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (HR(Q5vsQ1): 1.20; 95% CI: 1.04, 1.38; P for trend = 0.04) in men. Heme iron intake increased the risk of lung carcinoma in both men (HR(Q5vsQ1): 1.25; 95% CI: 1.07, 1.45; P for trend = 0.02) and women (HR(Q5vsQ1): 1.18; 95% CI: 0.99, 1.42; P for trend = 0.002). CONCLUSION: We observed a moderate association between meat consumption and lung carcinoma, which might be explained by heme iron intake, high-temperature cooking, and associated mutagens.

Epidemiologic assessment of sugars consumption using biomarkers: comparisons of obese and nonobese individuals in the European prospective investigation of cancer Norfolk.

We have previously shown that urinary sugars excretion in 24 h urine collections can serve as an independent biomarker of sugars consumption. In the European Prospective Investigation of Cancer (EPIC) Norfolk study of nutrition and cancer, this biomarker in spot urines has been assessed in a cross-sectional comparison of 404 obese individuals aged 45 to 75 years with a body mass index (BMI) of >30 kg/m(2) and 471 normal weight individuals aged 45 to 75 years with a BMI of <25 kg/m(2). In individuals of normal weight, sucrose, protein, and vitamin C intake were positively and highly significantly related to biomarkers in spot urine or plasma (P < 0.001), but there were no significant associations between biomarkers and food intake reports in the obese. Odds ratios for a BMI of >30 were significantly elevated for urinary sucrose [trend per milligram per liter quintile, 1.13; 95% confidence interval (95% CI), 1.02-1.25; P = 0.016], and the odds ratio for urinary sucrose/fructose ratio was highly significant (trend per quintile, 1.264; 95% CI, 1.142-1.401; P < 0.001). No associations for sugars intake and obesity were found using a food frequency questionnaire, and dietary vitamin C was apparently associated with increased risk (P < 0.001) despite an inverse association for plasma vitamin C. Nutritional biomarkers of consumption can complement existing methods for assessing cancer risk from diet in epidemiologic studies.

Urinary potassium is as reliable as urinary nitrogen for use as a recovery biomarker in dietary studies of free living individuals.

Twenty-four-hour urinary nitrogen (UN) is commonly used to validate dietary assessment methods. Potassium is more widespread in food than nitrogen, but the role of 24-h urinary potassium (UK) as a biomarker has been less studied and characterized. To investigate the performance of UK as a recovery biomarker compared with UN in subjects consuming their normal diet, 7 males and 6 females consumed known amounts of food based on their habitual varying diet (assessed beforehand from 4 consecutive 7-d food diaries) for 30 d in a metabolic suite. All daily urine samples and dietary duplicates were collected, and N and K were measured. Stool K was determined in 5-d pooled samples. Thirty-day mean analyzed K intake was 121.3 +/- 25.1 (mean +/- SD) mmol/d. Overall, 77 +/- 6.7% of K in the diet was excreted in urine and 18 +/- 5% in stool. Dietary K was correlated with UK (r = 0.89; P < 0.001). UN was 77.7 +/- 6.6% of N intake and was correlated with N in the diet (r = 0.87; P < 0.001). When 16 d of intake and 8 d of urine-collection measurements were randomly selected from the 30-d measurements, correlations were significant for both K (r = 0.86; P < 0.001) and N (r = 0.92; P < 0.001). The high correlations between UK and K in the diet show that UK is a reliable recovery biomarker for use in studies of dietary measurement error. Factors for use of urinary N as a recovery biomarker are also confirmed.

Urinary sucrose and fructose as biomarkers for sugar consumption.

The use of 24-hour urinary sucrose and fructose as potential biomarkers for sugars consumption was investigated in two studies of 21 healthy participants living in a volunteer suite where dietary intake was known and all specimens collected. The dose-response was assessed in 12 males using a randomized crossover design of three diets containing constant levels of 63, 143, and 264 g of sugars for 10 days each. Both sugars and sucrose intake were significantly correlated with the sum of sucrose and fructose concentration in urine (0.888; P < 0.001). To assess effects with volunteers consuming their habitual varying diets, seven males and six females were fed their usual diet (assessed beforehand from four consecutive self-completed 7-day food diaries) for 30 days under controlled conditions in the volunteer suite. The mean (+/-SD) calculated total sugars intake was 202 +/- 69 g/d, 41% from sucrose. Mean (+/-SD) urinary sucrose and fructose were 36.6 +/- 16.6 and 61.8 +/- 61.3 mg/d, respectively. The sum of sucrose and fructose in urine was significantly correlated with sugars (0.841; P < 0.001) and sucrose intake (0.773; P = 0.002). In the regression, 200 g of sugars intake predicted approximately 100 mg of sucrose and fructose in urine. The correlation between individual means of randomized 16 days of sugars intake and 8 days of sugars excretion data (as used in validation studies) remained as high as that obtained with the means of 30-day measurements and the regression estimates were very similar. Twenty-four-hour urinary sucrose and fructose could be grouped into a new category of biomarkers, predictive biomarkers, that can be used in studies determining the structure of dietary measurement error in free living individuals and to relate sugars intake to disease risk.