Tracking of serum lipid levels from childhood to adulthood: Systematic review and meta-analysis.

BACKGROUND AND AIMS: The utility of lipid screening in pediatric settings for preventing adult atherosclerotic cardiovascular diseases partly depends on the lifelong tracking of lipid levels. This systematic review aimed to quantify the tracking of lipid levels from childhood and adolescence to adulthood. METHODS: We systematically searched MEDLINE, Embase, Web of Science, and Google Scholar in March 2022. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; ID: CRD42020208859). We included cohort studies that measured tracking of lipids from childhood or adolescence (<18 years) to adulthood (≥18) with correlation or tracking coefficients. We estimated pooled correlation and tracking coefficients using random-effects meta-analysis. Risk of bias was assessed with a review-specific tool. RESULTS: Thirty-three studies of 19 cohorts (11,020 participants) were included. The degree of tracking from childhood and adolescence to adulthood differed among lipids. Tracking was observed for low-density lipoprotein cholesterol (pooled r = 0.55-0.65), total cholesterol (pooled r = 0.51-0.65), high-density lipoprotein cholesterol (pooled r = 0.46-0.57), and triglycerides (pooled r = 0.32-0.40). Only one study included tracking of non-high-density lipoprotein cholesterol (r = 0.42-0.59). Substantial heterogeneity was observed. Study risk of bias was moderate, mostly due to insufficient reporting and singular measurements at baseline and follow-up. CONCLUSIONS: Early-life lipid measurements are important for predicting adult levels. However, further research is needed to understand the tracking of non-high-density lipoprotein cholesterol and the stability of risk classification over time, which may further inform pediatric lipid screening and assessment strategies.

Modelling the replacement of red and processed meat with plant-based alternatives and the estimated effect on insulin sensitivity in a cohort of Australian adults.

Dietary guidelines are increasingly promoting mostly plant-based diets, limits on red meat consumption, and plant-based sources of protein for health and environmental reasons. It is unclear how the resulting food substitutions associate with insulin resistance, a risk factor for type 2 diabetes. We modelled the replacement of red and processed meat with plant-based alternatives and the estimated effect on insulin sensitivity. We included 783 participants (55 % female) from the Childhood Determinants of Adult Health study, a population-based cohort of Australians. In adulthood, diet was assessed at three time points using FFQ: 2004­2006, 2009­2011 and 2017­2019. We calculated the average daily intake of each food group in standard serves. Insulin sensitivity was estimated from fasting glucose and insulin concentrations in 2017­2019 (aged 39­49 years) using homoeostasis model assessment. Replacing red meat with a combination of plant-based alternatives was associated with higher insulin sensitivity (ß = 10·5 percentage points, 95 % CI (4·1, 17·4)). Adjustment for waist circumference attenuated this association by 61·7 %. Replacing red meat with either legumes, nuts/seeds or wholegrains was likewise associated with higher insulin sensitivity. Point estimates were similar but less precise when replacing processed meat with plant-based alternatives. Our modelling suggests that regularly replacing red meat, and possibly processed meat, with plant-based alternatives may associate with higher insulin sensitivity. Further, abdominal adiposity may be an important mediator in this relationship. Our findings support advice to prioritise plant-based sources of protein at the expense of red meat consumption.

A Healthful Plant-Based Eating Pattern Is Longitudinally Associated with Higher Insulin Sensitivity in Australian Adults.

BACKGROUND: A healthful plant-based eating pattern is associated with lower type 2 diabetes risk; however, the association with its preceding state, impaired insulin sensitivity, is less well established, particularly in younger populations with repeated measures of diet over time. OBJECTIVE: We aimed to examine the longitudinal relationship between a healthful plant-based eating pattern and insulin sensitivity in young to middle-aged adults. METHODS: We included 667 participants from the Childhood Determinants of Adult Health (CDAH) study, a population-based cohort in Australia. Healthful plant-based diet index (hPDI) scores were derived from food frequency questionnaire data. Plant foods considered "healthful" were scored positively (e.g., whole grains, fruit, vegetables), with all remaining foods scored reversely (e.g., refined grains, soft drinks, meat). Updated homeostatic model assessment (HOMA2) estimated insulin sensitivity from fasting insulin and glucose concentrations. We used linear mixed-effects regression to analyze data from 2 time points: CDAH-1 (2004-2006, 26-36 y of age) and CDAH-3 (2017-2019, 36-49 y of age). hPDI scores were modeled as between- and within-person effects (i.e., a participant's overall mean and their deviation from said mean at each time point, respectively). RESULTS: The median follow-up duration was 13 y. In our primary analysis, each 10-unit difference in hPDI score was associated with higher log-HOMA2 insulin sensitivity [95% confidence interval], with between-person (ß = 0.11 [0.05, 0.17], P < 0.001) and within-person effects (ß = 0.10 [0.04, 0.16], P = 0.001). The within-person effect persisted despite accounting for compliance with dietary guidelines. Adjustment for waist circumference attenuated the between-person effect by 70% (P = 0.26) and the within-person effect by 40% (P = 0.04). CONCLUSIONS: In young to middle-aged Australian adults, a healthful plant-based eating pattern (determined using hPDI scores) was longitudinally associated with higher insulin sensitivity, and therefore, potentially lower type 2 diabetes risk later in life.

Retrospectively Estimating Energy Intake and Misreporting From a Qualitative Food Frequency Questionnaire: An Example Using Australian Cohort and National Survey Data.

Qualitative food frequency questionnaires (Q-FFQ) omit portion size information from dietary assessment. This restricts researchers to consumption frequency data, limiting investigations of dietary composition (i.e., energy-adjusted intakes) and misreporting. To support such researchers, we provide an instructive example of Q-FFQ energy intake estimation that derives typical portion size information from a reference survey population and evaluates misreporting. A sample of 1,919 Childhood Determinants of Adult Health Study (CDAH) participants aged 26-36 years completed a 127-item Q-FFQ. We assumed sex-specific portion sizes for Q-FFQ items using 24-h dietary recall data from the 2011-2012 Australian National Nutrition and Physical Activity Survey (NNPAS) and compiled energy density values primarily using the Australian Food Composition Database. Total energy intake estimation was daily equivalent frequency × portion size (g) × energy density (kJ/g) for each Q-FFQ item, summed. We benchmarked energy intake estimates against a weighted sample of age-matched NNPAS respondents (n = 1,383). Median (interquartile range) energy intake was 9,400 (7,580-11,969) kJ/day in CDAH and 9,055 (6,916-11,825) kJ/day in weighted NNPAS. Median energy intake to basal metabolic rate ratios were 1.43 (1.15-1.78) in CDAH and 1.35 (1.03-1.74) in weighted NNPAS, indicating notable underreporting in both samples, with increased levels of underreporting among the overweight and obese. Using the Goldberg and predicted total energy expenditure methods for classifying misreporting, 65 and 41% of CDAH participants had acceptable/plausible energy intake estimates, respectively. Excluding suspected CDAH misreporters improved the plausibility of energy intake estimates, concordant with expected body weight associations. This process can assist researchers wanting an estimate of energy intake from a Q-FFQ and to evaluate misreporting, broadening the scope of diet-disease investigations that depend on consumption frequency data.