13 research outputs found
Larval diet affects mosquito development and permissiveness to Plasmodium infection
No full textThe larval stages of malaria vector mosquitoes
develop in water pools, feeding mostly on
microorganisms and environmental detritus. Richness in the nutrient supply to larvae influences the
development and metabolism of larvae and adults. Here, we investigated the effects of larval diet on
the development, microbiota content and permissiveness to
Plasmodium
of
Anopheles coluzzii
. We
tested three fish diets often used to rear mosquitoes
in the laboratory, including two pelleted diets,
Dr. Clarke’s Pool Pellets
and
Nishikoi Fish Pellets
, and one flaked diet,
Tetramin Fish-Flakes.
Larvae
grow and develop faster and produce bigger adults when feeding on both types of pellets compared
with flakes. This correlates with a higher microbio
ta load in pellet-fed larv
ae, in agreement with the
known positive effect of the microbiota on mosq
uito development. Larval diet also significantly
influences the prevalence and intensity of
Plasmodium berghei
infection in adults, whereby
Nishikoi
Fish Pellets
-fed larvae develop into adults that are highly permissive to parasites and survive longer
after infection
.
This correlates with a lower amount of
Enterobacteriaceae
in the midgut microbiota.
Together, our results shed light on the influenc
e of larval feeding on mosquito development,
microbiota and vector competence
; they also provide useful data for mosquito rearing
The Big Poo Review: A ZOE Health Study Deep Dive into the UK’s Bowel Habits
No full textBackground: Bowel habits remain under-studied despite their associations with chronic diseases and their impact on quality of life. We aimed to elucidate the pattern of bowel habits in the UK and investigate gender differences and dietary associations. Methods: A UK population-based survey, “The Big Poo Review,” involving 142,765 participants, was conducted in the ZOE Health Study (LRS/DP-20/21-25809). Respondents completed a 37-item bowel habit questionnaire. Diarrhoea was defined as evacuation >3 times/day or passing Bristol Stool scale (BSS) type 6 or 7 > 25% and constipation was defined as evacuation <3 times/wk or passing BSS type 1 or 2 > 25%. Participants (n = 26,703) who completed a food frequency questionnaire within 5 months of the study were included in the subgroup dietary analysis. Results: Participants were predominantly female (77%) with a mean age of 57.8 years (IQR: 50–67). The most frequently reported bowel pattern was a single daily bowel movement (54%) after breakfast (60%) and BSS type 4 (40%). The mean defecation frequency was 1.7 times/day (SD 0.9), but 0.4% of participants defecated <1 time/wk and 1.4% defecated >4 times/day. Constipation was reported in 21.0% (women 23.3%, men 13.0%; p < 0.001) and diarrhoea in 15.3% (men 17.5%, women 14.7%; p < 0.001). Those with diarrhoea or constipation consumed significantly fewer legumes, nuts, and seeds (12 g and 7 g/day less, respectively), fruits (14 g and 18 g/day less, respectively), and vegetables (14 g and 30 g/day less, respectively) than those without (p < 0.01 for all comparisons). Dairy intake was different between all three groups (constipation 276 g/day; diarrhoea 256 g/day; regular stools 267 g/day; p < 0.001 for all comparisons). Discussion: This survey is the largest study of UK bowel habits to date, highlighting gender and dietary differences in habits. The high prevalence of constipation and diarrhoea underscores the need for focused public health efforts and potential nutrition interventions
Improved Cardiometabolic Health Using a Personalised Nutrition Approach: The ZOE METHOD Study
No full textBackground: Large variability exists in people’s responses to foods [...
Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition
No full textBACKGROUND: Continuous glucose monitor (CGM) devices enable characterization of individuals’ glycemic variation. However, there are concerns about their reliability for categorizing glycemic responses to foods that would limit their potential application in personalized nutrition recommendations. OBJECTIVES: We aimed to evaluate the concordance of 2 simultaneously worn CGM devices in measuring postprandial glycemic responses. METHODS: Within ZOE PREDICT (Personalised Responses to Dietary Composition Trial) 1, 394 participants wore 2 CGM devices simultaneously [n = 360 participants with 2 Abbott Freestyle Libre Pro (FSL) devices; n = 34 participants with both FSL and Dexcom G6] for ≤14 d while consuming standardized (n = 4457) and ad libitum (n = 5738) meals. We examined the CV and correlation of the incremental area under the glucose curve at 2 h (glucose(iAUC0–2 h)). Within-subject meal ranking was assessed using Kendall τ rank correlation. Concordance between paired devices in time in range according to the American Diabetes Association cutoffs (TIR(ADA)) and glucose variability (glucose CV) was also investigated. RESULTS: The CV of glucose(iAUC0–2 h) for standardized meals was 3.7% (IQR: 1.7%–7.1%) for intrabrand device and 12.5% (IQR: 5.1%–24.8%) for interbrand device comparisons. Similar estimates were observed for ad libitum meals, with intrabrand and interbrand device CVs of glucose(iAUC0–2 h) of 4.1% (IQR: 1.8%–7.1%) and 16.6% (IQR: 5.5%–30.7%), respectively. Kendall τ rank correlation showed glucose(iAUC0–2h)-derived meal rankings were agreeable between paired CGM devices (intrabrand: 0.9; IQR: 0.8–0.9; interbrand: 0.7; IQR: 0.5–0.8). Paired CGMs also showed strong concordance for TIR(ADA) with a intrabrand device CV of 4.8% (IQR: 1.9%–9.8%) and an interbrand device CV of 3.2% (IQR: 1.1%–6.2%). CONCLUSIONS: Our data demonstrate strong concordance of CGM devices in monitoring glycemic responses and suggest their potential use in personalized nutrition. This trial was registered at clinicaltrials.gov as NCT03479866
Empowering global nutrition with digital technology : practical implementation in clinical practice and research
No full textPeer reviewe
Author Correction: Postprandial glycaemic dips predict appetite and energy intake in healthy individuals
No full textCharacterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study
No full textBackground: Postprandial metabolomic profiles and their inter-individual variability are not well characterised. Here, we describe postprandial metabolite changes, their correlations with fasting values and their inter- and intra-individual variability, following a standardised meal in the ZOE PREDICT 1 cohort. Methods: In the ZOE PREDICT 1 study (n = 1002 (NCT03479866)), 250 metabolites, mainly lipids, were measured by a Nightingale NMR panel in fasting and postprandial (4 and 6 h after a 3.7 MJ mixed nutrient meal, with a second 2.2 MJ mixed nutrient meal at 4 h) serum samples. For each metabolite, inter- and intra-individual variability over time was evaluated using linear mixed modelling and intraclass correlation coefficients (ICC) were calculated. Results: Postprandially, 85% (of 250 metabolites) significantly changed from fasting at 6 h (47% increased, 53% decreased; Kruskal–Wallis), with 37 measures increasing by >25% and 14 increasing by >50%. The largest changes were observed in very large lipoprotein particles and ketone bodies. Seventy-one percent of circulating metabolites were strongly correlated (Spearman’s rho >0.80) between fasting and postprandial timepoints, and 5% were weakly correlated (rh
Postprandial glycaemic dips predict appetite and energy intake in healthy individuals
No full textUnderstanding how to modulate appetite in humans is key to developing successful weight loss interventions. Here, we showed that postprandial glucose dips 2–3 h after a meal are a better predictor of postprandial self-reported hunger and subsequent energy intake than peak glucose at 0–2 h and glucose incremental area under the blood glucose curve at 0–2 h. We explore the links among postprandial glucose, appetite and subsequent energy intake in 1,070 participants from a UK exploratory and US validation cohort, who consumed 8,624 standardized meals followed by 71,715 ad libitum meals, using continuous glucose monitors to record postprandial glycaemia. For participants eating each of the standardized meals, the average postprandial glucose dip at 2–3 h relative to baseline level predicted an increase in hunger at 2–3 h (r = 0.16, P < 0.001), shorter time until next meal (r = −0.14, P < 0.001), greater energy intake at 3–4 h (r = 0.19, P < 0.001) and greater energy intake at 24 h (r = 0.27, P < 0.001). Results were directionally consistent in the US validation cohort. These data provide a quantitative assessment of the relevance of postprandial glycaemia in appetite and energy intake modulation
Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial
No full textLarge variability exists in people’s responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020–2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants (n = 347), aged 41–70 years and generally representative of the average US population, were randomized to the PDP (n = 177) or control (n = 170). Intention-to-treat analysis (n = 347) between groups showed significant reduction in TGs (mean difference = −0.13 mmol l−1; log-transformed 95% confidence interval = −0.07 to −0.01, P = 0.016). Changes in low-density lipoprotein cholesterol were not significant. There were improvements in secondary outcomes, including body weight, waist circumference, HbA1c, diet quality and microbiome (beta-diversity) (P < 0.05), particularly in highly adherent PDP participants. However, blood pressure, insulin, glucose, C-peptide, apolipoprotein A1 and B, and postprandial TGs did not differ between groups. No serious intervention-related adverse events were reported. Following a personalized diet led to some improvements in cardiometabolic health compared to standard dietary advice
Human postprandial responses to food and potential for precision nutrition
No full textMetabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866
