Abdominal and general adiposity are independently associated with mortality, but there is no consensus on how best to assess abdominal adiposity. We compared the ability of alternative waist indices to complement body mass index (BMI) when assessing all-cause mortality. We used data from 352,985 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) and Cox proportional hazards models adjusted for other risk factors. During a mean follow-up of 16.1 years, 38,178 participants died. Combining in one model BMI and a strongly correlated waist index altered the association patterns with mortality, to a predominantly negative association for BMI and a stronger positive association for the waist index, while combining BMI with the uncorrelated A Body Shape Index (ABSI) preserved the association patterns. Sex-specific cohort-wide quartiles of waist indices correlated with BMI could not separate high-risk from low-risk individuals within underweight (BMI < 18.5 kg/m2) or obese (BMI ≥ 30 kg/m2) categories, while the highest quartile of ABSI separated 18–39% of the individuals within each BMI category, which had 22–55% higher risk of death. In conclusion, only a waist index independent of BMI by design, such as ABSI, complements BMI and enables efficient risk stratification, which could facilitate personalisation of screening, treatment and monitoring.
Cadmium (Cd) is a toxic heavy metal that can contribute to numerous diseases as well as increased mortality. Diet is the primary source of Cd exposure for most individuals, yet little is known about the foods and food groups that contribute most substantially to dietary Cd intake in the US. Therefore, the objective of this study was to estimate dietary Cd intake and identify major food sources of Cd in the US population and among subgroups of the population. Individuals aged 2 years and older from the National Health and Nutrition Examination Survey (NHANES) 2007–2012 were included in this study (n = 12,523). Cd intakes were estimated from two days of 24-h dietary recalls by matching intake data with the Cd database of the Food and Drug Administration (FDA)’s Total Diet Study 2006 through 2013. The average dietary Cd consumption in the population was 4.63 μg/day, or 0.54 μg/kg body weight/week, which is 22% of the tolerable weekly intake (TWI) of 2.5 μg/kg body weight/week. Greater daily Cd intakes were observed in older adults, males, those with higher income, higher education, or higher body mass index. The highest Cd intakes on a body weight basis were observed in children 10 years and younger (38% of TWI), underweight individuals (38% of TWI), and alcohol non-consumers (24% of TWI). The food groups that contributed most to Cd intake were cereals and bread (34%), leafy vegetables (20%), potatoes (11%), legumes and nuts (7%), and stem/root vegetables (6%). The foods that contributed most to total Cd intake were lettuce (14%), spaghetti (8%), bread (7%), and potatoes (6%). Lettuce was the major Cd source for Caucasians and Blacks, whereas tortillas were the top source for Hispanics, and rice was the top contributor among other ethnic subgroups including Asians. This study provides important information on the dietary Cd exposure of Americans, and identifies the groups with the greatest dietary Cd exposure as well as the major sources of dietary Cd among sociodemographic subgroups.
Purpose There is inconsistent evidence regarding the relationship between higher intake of nuts, being an energy-dense food, and weight gain. We investigated the relationship between nut intake and changes in weight over 5 years. Methods This study includes 373,293 men and women, 25–70 years old, recruited between 1992 and 2000 from 10 European countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Habitual intake of nuts including peanuts, together defined as nut intake, was estimated from country-specific validated dietary questionnaires. Body weight was measured at recruitment and self-reported 5 years later. The association between nut intake and body weight change was estimated using multilevel mixed linear regression models with center/country as random effect and nut intake and relevant confounders as fixed effects. The relative risk (RR) of becoming overweight or obese after 5 years was investigated using multivariate Poisson regressions stratified according to baseline body mass index (BMI). Results On average, study participants gained 2.1 kg (SD 5.0 kg) over 5 years. Compared to non-consumers, subjects in the highest quartile of nut intake had less weight gain over 5 years (−0.07 kg; 95% CI −0.12 to −0.02) (P trend = 0.025) and had 5% lower risk of becoming overweight (RR 0.95; 95% CI 0.92–0.98) or obese (RR 0.95; 95% CI 0.90–0.99) (both P trend <0.008). Conclusions Higher intake of nuts is associated with reduced weight gain and a lower risk of becoming overweight or obese.
Polyphenols have been shown to exert biological activity in experimental models of colon cancer; however, human data linking specific polyphenols to colon cancer is limited. We assessed the relationship between pre‐diagnostic plasma polyphenols and colon cancer risk in a case–control study nested within the European Prospective Investigation into Cancer and Nutrition study. Using high pressure liquid chromatography coupled to tandem mass spectrometry, we measured concentrations of 35 polyphenols in plasma from 809 incident colon cancer cases and 809 matched controls. We used multivariable adjusted conditional logistic regression models that included established colon cancer risk factors. The false discovery rate (qvalues) was computed to control for multiple comparisons. All statistical tests were two‐sided. After false discovery rate correction and in continuous log2‐transformed multivariable models, equol (odds ratio [OR] per log2‐value, 0.86, 95% confidence interval [95% CI] = 0.79–0.93;qvalue = 0.01) and homovanillic acid (OR per log2‐value, 1.46, 95% CI = 1.16–1.84;qvalue = 0.02) were associated with colon cancer risk. Comparing extreme fifths, equol concentrations were inversely associated with colon cancer risk (OR = 0.61, 95% CI = 0.41–0.91,ptrend = 0.003), while homovanillic acid concentrations were positively associated with colon cancer development (OR = 1.72, 95% CI = 1.17–2.53,ptrend < 0.0001). No heterogeneity for these associations was observed by sex and across other colon cancer risk factors. The remaining polyphenols were not associated with colon cancer risk. Higher equol concentrations were associated with lower risk, and higher homovanillic acid concentrations were associated with greater risk of colon cancer. These findings support a potential role for specific polyphenols in colon tumorigenesis.
We investigated associations of habitual dietary intake with the taxonomic composition and diversity of the human gut microbiota in 222 Koreans aged 18–58 years in a cross-sectional study. Gut microbiota data were obtained by 16S rRNA gene sequencing on DNA extracted from fecal samples. The habitual diet for the previous year was assessed by a food frequency questionnaire. After multivariable adjustment, intake of several food groups including vegetables, fermented legumes, legumes, dairy products, processed meat, and non-alcoholic beverages were associated with major phyla of the gut microbiota. A dietary pattern related to higher α-diversity (HiαDP) derived by reduced rank regression was characterized by higher intakes of fermented legumes, vegetables, seaweeds, and nuts/seeds and lower intakes of non-alcoholic beverages. The HiαDP was positively associated with several genera of Firmicutes such as Lactobacillus, Ruminococcus, and Eubacterium (all p < 0.05). Among enterotypes identified by principal coordinate analysis based on the β-diversity, the Ruminococcus enterotype had higher HiαDP scores and was strongly positively associated with intakes of vegetables, seaweeds, and nuts/seeds, compared to the two other enterotypes. We conclude that a plant- and fermented food-based diet was positively associated with some genera of Firmicutes (e.g., Lactobacillus, Ruminococcus, and Eubacterium) reflecting better gut microbial health.