Intake of Potatoes Is Associated with Higher Diet Quality, and Improved Nutrient Intake and Adequacy among US Adolescents: NHANES 2001-2018 Analysis

Perspective: Potatoes, Quality Carbohydrates, and Dietary Patterns

Potatoes have long been a staple food in many cultures and cuisines, but they have gained a reputation as a low-quality carbohydrate source that should be avoided in the diet. Historically, this view has been justified by citing the glycemic index of potatoes as the main indicator of their quality. However, their nutrient composition should also be considered. The association of potatoes with energy-dense Western dietary patterns has also contributed to a perception that potatoes are inherently unhealthy. Although some studies have suggested an association between potato consumption and increased risk of health problems, such as type 2 diabetes, these associations may be confounded by fried potato intake and are strongest at intake levels higher than average consumption rates. Epidemiologic data suggest total potato intake is not a health risk in Eastern populations and can be consumed as part of a healthy diet. Furthermore, clinical trial data demonstrate that potatoes' health impact, irrespective of preparation, is similar to legumes and comparable with refined grains, with few deleterious effects found. These findings highlight the importance of moving beyond the glycemic index and adopting a more nuanced evaluation of the epidemiologic data to better understand the health impact of potato intake. Ultimately, the negative reputation of potatoes stems from an overinterpretation of their glycemic index and association with unhealthy Western dietary patterns, as well as oversimplification of the epidemiologic data. By considering carbohydrate quality, it becomes clear that potatoes can be part of a healthy diet given the proper consideration.

Association between per- and polyfluoroalkyl substances (PFAS) and depression in U.S. adults: A cross-sectional study of NHANES from 2005 to 2018

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS) are widespread persistent organic pollutants (POPs) associated with diseases including osteoporosis, altered immune function and cancer. However, few studies have investigated the association between PFAS mixture exposure and Depression in general populations.

METHODS

Nationally representative data from the National Health and Nutrition Examination Survey (NHANES) (2005-2018) were used to analyze the association between PFAS and Depression in U.S. adults. Total 12,239 adults aged 20 years or older who had serum PFAS measured and answered Patient Health Questionnaire-9 (PHQ-9) were enrolled in this study. PFAS monomers detected in all 7 investigation cycles were included in the study. Generalized additive model (GAM) was used to fit smooth curves and threshold effect analysis was carried out to find the turning point of smooth curves. Generalized linear model (GLM) was used to describe the non-linear relationship between PFAS and depression and unconditioned logistic regression was used to risk analysis.

RESULTS

The median of total serum PFAS concentration was 14.54 ng/mL. The curve fitting results indicated a U-shaped relationship between total serum PFAS and depression: PFAS< 39.66 ng/mL, A negative correlation between PHQ-9 score and serum PFAS concentration was observed (β 0.047,95%CI -0.059, -0.036). The depression PHQ-9 score decreased with the increase of serum PFAS concentration. PFAS ≥ 39.66 ng/mL, A positive correlation was observed between PFAS and PHQ-9 score (β 0.010,95% CI 0.003, 0.017). The depression PHQ-9 score increased with the increase of serum PFAS concentration.

CONCLUSIONS

Our study provides new clues to the association of PFAS with depression, and large population-based cohort studies that can validate the causal association as well as toxicological mechanism studies are needed for validation.

Certain dietary patterns including potatoes are associated with higher and lower diet quality and physiological measures in children and adults, NHANES 2001-2018

A large percentage of daily vegetable intake is attributed to white potatoes, but limited information is available on how potatoes are incorporated into dietary patterns in the US. Therefore, the objective of this study was to determine food patterns that include potatoes and to compare the associated diet quality and association with biomarkers to a food pattern without potatoes. Data from American subjects 2-18 and 19 years and older who participated in the What We Eat in America portion of the National Health and Nutrition Examination Survey cycles 2001-2018 were utilized in the current study. Diet quality was assessed using the Healthy Eating Index-2015. Anthropometric variables included body mass index (BMI), waist circumference, and weight. Biomarkers analyzed included glucose, insulin, triglycerides, HDL-, LDL-, and total cholesterol. Multiple food clusters containing potatoes were identified with several having higher and lower diet quality as compared to a food pattern without potatoes. Children and adolescents in one potato cluster had lower BMI, waist circumference, and body weight compared to those in a no potato dietary pattern, whereas adults in 3 potato clusters had higher anthropometric variables than those in a no potato pattern. In adults, some dietary patterns including potatoes were also associated with lower and higher HDL and total cholesterol and higher insulin levels. The percentage of calories from potatoes across patterns was small, ∼9-12%, suggesting the differences observed in diet quality and biomarkers were due to other food categories consumed in the pattern. This study suggests there are ways to incorporate potatoes as part of a healthy eating pattern but depends more on the other foods included in the diet.

Metabolic Syndrome Is Associated with Low Diet Quality Assessed by the Healthy Eating Index-2015 (HEI-2015) and Low Concentrations of High-Density Lipoprotein Cholesterol

Presenting diet quality of patients with metabolic syndrome (MetS), using a holistic approach is more useful than investigating dietary individual components, but there is still a small amount of research in this area. The aim of this study assessed the diet quality, as measured by the HEI-2015, of MetS patients compared to healthy individuals. The study and control group consisted of 215 patients with MetS and 320 people without MetS, respectively. A nutritional analysis using a semi-quantitative food frequency questionnaire was used to evaluate the nutritional habits in the study and control group. Total HEI-2015 scores were significantly lower in MetS subjects than in those in the control group (65.04 ± 9.71 vs. 66.75 ± 8.88) and the quality of women’s diets was better than the quality of men’s diet (66.83 ± 8.99 vs. 64.75 ± 9.57). We also observed that low HDL-c concentration increased the risk of MetS in the general population the most. Across the population, there was a weak positive correlation between HDL-c concentrations and total HEI-2015 scores and a weak negative correlation between mean waist circumference values and total HEI-2015 scores. HDL-c concentrations may be a key factor in the prevention of MetS and appropriate therapeutic management to increase HDL-c levels may be of key importance in patients diagnosed with MetS.

Associations of Serum Total 25OHD, 25OHD3, and epi-25OHD3 with Insulin Resistance: Cross-Sectional Analysis of the National Health and Nutrition Examination Survey, 2011–2016

Background: Vitamin D may have a role in insulin sensitivity. However, the data on the association between various metabolites of Vitamin D and insulin-related parameters have been limited. Methods: We identified 6026 adults aged 20–80 years who participated in the 2011–2016 National Health and Nutrition Examination Survey (NHANES). Serum total 25OHD, 25OHD3, and epi-25OHD3, fasting glucose, insulin, and HOMA2-IR were obtained from the NHANES data. The association between serum Vitamin D-related values and insulin resistance was analyzed using a generalized linear model. For risk analysis, multifactorial logistic regression was used. Results: The median total 25-hydroxyvitamin D level, 25-hydroxyvitamin D3 level, and 3-epi-25-hydroxyvitamin D3 level were 62.5 nmol/L, 58.8 nmol/L, and 3.3 nmol/L, respectively. After adjustment for sex, age, race, ethnicity, and education status, the ORs for the insulin resistance of participants of total 25-hydroxyvitamin D, 25-hydroxyvitamin D3, and 3-epi-25-hydroxyvitamin D3 were 0.32 (95% CI 0.24, 0.43), 0.34 (95% CI 0.26, 0.44), and 0.64 (95% CI 0.53, 0.77), respectively. After an adjustment for body mass index, diabetes, and drinking and smoking, the ORs for the insulin resistance of the participants for total 25-hydroxyvitamin D, 25-hydroxyvitamin D3, and 3-epi-25-hydroxyvitamin D3 were 0.56 (95% CI 0.40, 0.78), 0.63 (95% CI 0.46, 0.85), and 0.99 (95% CI 0.80, 1.24), respectively. Conclusions: Our study provides suggestive evidence for the association between Vitamin D concentrations and a lower risk of insulin resistance. Evidence from larger and more adequately powered cohort studies is needed to confirm our results.

Multiple Metrics of Carbohydrate Quality Place Starchy Vegetables Alongside Non-starchy Vegetables, Legumes, and Whole Fruit

Background

Starchy vegetables, including white potatoes, are often categorized as “lower-quality” carbohydrate foods, along with refined grains, 100% fruit juices, sweetened beverages, and sugars, snacks and sweets. Among “higher-quality” carbohydrates are whole grains, non-starchy vegetables, legumes, and whole fruits.

Objective

To apply multiple nutrient profiling (NP) models of carbohydrate quality to foods containing &gt;40% carbohydrate by dry weight in the USDA Food and Nutrient Database for Dietary Studies (FNDDS 2017-18).

Methods

Carbohydrate foods in the FNDDS (n = 2423) were screened using four recent Carbohydrate Quality Indices (CQI) and a new Carbohydrate Food Quality Score (CFQS-4). Cereal products containing &gt;25% whole grains by dry weight were classified as whole grain foods.

Results

Based on percent items meeting the criteria for 4 CQI scores, legumes, non-starchy and starchy vegetables, whole fruit, and whole grain foods qualified as “high quality” carbohydrate foods. Distribution of mean CFQS-4 values showed that starchy vegetables, including white potatoes placed closer to non-starchy vegetables and fruit than to candy and soda.

Conclusion

Published a priori determinations of carbohydrate quality do not always correspond to published carbohydrate quality metrics. Based on CQI metrics, specifically designed to assess carbohydrate quality, starchy vegetables, including white potatoes, merit a category reassignment and a more prominent place in dietary guidance.

Association of urinary arsenic with insulin resistance: Cross-sectional analysis of the National Health and Nutrition Examination Survey, 2015-2016

BACKGROUND

Long-term arsenic exposure is associated with diabetes in adults, the mechanism of which involves insulin resistance. The relationship between arsenic and insulin resistance in adults is unclear. We analyzed the relationship between urinary arsenic and insulin resistance in US adults.

RESULTS

We identified 815 adults aged 20-79 years who participated in the 2015-2016 National Health and Nutrition Examination Survey (NHANES). Urinary arsenic, fasting glucose, serum insulin, and other key covariates were obtained from the NHANES data. The association between urinary arsenic and insulin resistance was evaluated by analyzing the urinary arsenic level and homeostasis model assessment-insulin resistance. The median total urinary arsenic level was 6.82 µg/L. After adjusting for possible confounding factors (gender, age, and body mass index), the 80th and 20th percentile odds ratio (OR) was 1.41 (95% confidence interval [CI] 1.07, 1.87); the OR of the 70th and 30th percentiles was 1.41 (95% CI 1.08, 1.84).

CONCLUSIONS

In most subgroups, after similar adjustment, the relationship between urine total arsenic and insulin resistance remained. Total arsenic exposure in urine may be associated with insulin resistance. Evidence from larger and more adequately powered cohort studies is needed to confirm our results.