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Biology subjects

Kuhnle, G. G. C.

Publications and source records attributed to Kuhnle, G. G. C..

2 recordsLinked to original sources

Reliance on self-reports and estimated food composition data in nutrition research introduces significant bias that can only be addressed with biomarkers

The chemical composition of foods is complex, variable, and dependent on many factors. This has a major impact on nutrition research as it foundationally affects our ability to adequately assess actual intake of nutrients and other compounds. Despite of this, accurate data on nutrient intake are key for investigating associations and causal relationships between intake, health, and disease risk at the service of developing evidence-based dietary guidance that enables improvements in population health. Here, we exemplify the importance of this challenge by investigating the impact of food content variability on nutrition research using three bioactives as model: flavan-3-ols, (-)-epicatechin, and nitrate. Our results show that common approaches aimed at addressing the high compositional variability of even the same foods impede the accurate assessment of nutrient intake, generally. This suggests that the results of many nutrition studies using food composition data are potentially unreliable and carry greater limitations than commonly appreciated, consequently resulting in dietary recommendations with significant limitations and unreliable impact on public health. Thus, current challenges related to nutrient intake assessments need to be addressed and mitigated by the development of improved dietary assessment methods involving the use of nutritional biomarkers.

physiology↗

Association between urinary biomarkers of total sugars and sucrose intake and BMI in a cross-sectional study

Obesity is an important modifiable risk factors for chronic diseases. While there is increasing focus on the role of dietary sugars, there remains a paucity of data establishing the association between sugar intake and obesity in the general public. The objective of this study was to investigate associations of estimated sugar intake with odds for obesity in a representative samples of English adults. We used data from 434 participants of the 2005 Health Survey of England. Biomarkers for total sugar intake were measured in 24h urine samples and used to estimate intake. Linear and logistic regression analyses were used to investigate associations between estimated intake and measures of obesity (BMI, waist circumference and waist-to-hip ratio) and obesity risk., respectively. Estimated sugars intake was significantly associated with BMI, waist circumference and waist-to-hip ratio, and these associations remained significant after adjustment for estimated protein intake. Estimated sugars intake was also associated with increased odds for obesity based on BMI (OR 1.02; 95% CI 1.00; 1.04 per 10 g), waist-circumference (OR 1.03; 95% CI 1.01; 1.05) and waist-to-hip ratio (OR 1.04; 95% CI 1.02; 1.06); all OR estimates remained significant after adjusting for estimated protein intake. Our results show a significant association between biomarker-estimated total sugars intake and both measures of obesity and obesity risk, confirming positive associations between total sugar intake, measures of obesity and obesity risk. This biomarker could be used to monitor the efficacy of public health interventions.

epidemiology↗