Dietary outcomes within the study of novel approaches to weight gain prevention (SNAP) randomized controlled trial

Effectiveness of diet quality indices in measuring a change in diet quality over time: a systematic review and meta-analysis of randomized controlled trials

CONTEXT

Diet quality indices (DQIs) were developed to score and rank adherence to dietary patterns in observational studies, but their use to measure changes in diet quality in intervention trials is becoming common in the literature.

OBJECTIVE

This systematic review and meta-analysis aimed to assess the effectiveness of DQIs to measure change in diet quality in intervention trials.

DATA SOURCES

MEDLINE, CINAHL, Embase, and the Cochrane Central Register of Controlled Trials databases were searched from January 1994 to June 2020. Two reviewers independently completed full-text screening. Eligible studies were randomized controlled trials that used validated a priori DQIs to measure change in diet quality in adults.

DATA EXTRACTION

Data were extracted by an independent reviewer and reviewed by the research team. Risk of bias was assessed by the Cochrane Collaboration's Risk of Bias 2.0 tool.

DATA ANALYSIS

The 34 included studies (52% of reviewed studies, 0.6% of initially identified studies) used 10 different DQIs, 7 of which were able to measure significant change in diet quality. Meta-analyses of pooled results demonstrated change in the Healthy Eating Index (MD 5.35; 95%CI, 2.74-7.97; P < 0.001) and the Mediterranean Dietary Adherence Screener (MD 1.61; 95%CI, 1.00-2.23; P < 0.001) scores. DQIs were more likely to measure change in diet quality if they reflected the diet pattern being implemented, if the intervention was significantly different from the baseline and control diets, and if the study was adequately powered to detect change.

CONCLUSION

DQIs are responsive to change in diet quality in intervention trials when the index used reflects the dietary changes made and the study is adequately powered. The appropriate selection of a DQI to suitably match dietary changes and study populations is important for future dietary intervention trials.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration no. CRD42020181357.

Plasma Fatty Acids, Not Dietary Fatty Acids, Associated with Obesity in Four Ethnic Minority Groups Unique to Southwest China: A Cross-Sectional Study

BACKGROUND

Dietary fatty acids (DFAs) and plasma fatty acids (PFAs) are linked to obesity. However, whether this association exists among ethnic minorities remains lacking. The present cross-sectional study was designed to investigate the correlation between DFAs, PFAs and obesity in four ethnic minority groups to Southwest China.

METHODS

A total of 166 obese people, and 166 normal-BMI subjects matched based on their age-, sex-, and ethnicity- were recruited from four different ethnic minority groups. DFAs were obtained through food frequency questionnaires. PFAs were assayed by GC/MS method. Binary and multiple regression analyses were performed to evaluate the correlation among DFAs, PFAs and obesity. Canonical correlation analysis (CCA) was conducted to assess the relationship between DFAs and PFAs.

RESULTS

FAs were found to be highest in the Naxi people and lowest in the Hani people. Multiple logistic regression analysis revealed that plasma C16:0 (OR = 1.310; 95% CI, 1.028-1.669) in the Hani people; plasma C20:3 n-6 (OR = 6.250; 95% CI, 1.224-31.927) and dietary C20:1 (OR = 9.231; 95% CI, 1.253-68.016) in the Wa people; plasma C18:0 (OR = 0.788; 95% CI, 0.681-0.912) in the Naxi people were seen to be independent predictive factors for obesity. CCA showed that DFAs were positively correlated with PFAs in the Naxi (r: 0.676; P < 0.05) and Bulang people (r: 0.897; P < 0.05), but there was no correlation in the Hani and Wa people.

CONCLUSION

In this study, PFAs but not DFAs were independently associated with obesity, and different among the four ethnic minorities.

Patterns of weight change in a weight gain prevention study for young adults

OBJECTIVE

Recovery from weight regain is uncommon during weight loss treatment. This study examined whether participants in a weight gain prevention intervention similarly struggle to recover following weight gains and which factors predict transitions.

METHODS

This is a secondary analysis of data from the Study of Novel Approaches to Weight Gain Prevention (SNAP), a randomized controlled trial comparing two weight gain prevention interventions with a control group. Young adults (n = 599; age 18-35 years) were followed over 3 years. Markov models identified transition rates in going above and returning below baseline weight across follow-up. Logistic regressions identified predictors of transitions.

RESULTS

At each time point, approximately double the number of participants who transitioned from below to above baseline transitioned from above to below. The magnitude of weight changes from baseline and the number of weight loss strategies used predicted transitions from below to above and above to below baseline weight (with opposite relationships). Infrequent self-weighing and lower dietary restraint predicted transitions below to above baseline weight. Treatment arm, demographics, calorie consumption, and physical activity generally did not predict transitions.

CONCLUSIONS

Young adults engaging in weight gain prevention struggle to lose gained weight. Alternative strategies are needed to address weight gains in weight gain prevention interventions.

Preventing weight gain in adults: A systematic review and meta-analysis of randomized controlled trials

Weight gain prevention is a global public health priority to address escalating adiposity in adults. This review evaluates the efficacy of weight gain prevention trials targeting adults aged 18-50 years and adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Trials of any duration from inception to May 2020 that evaluated a weight gain prevention intervention (using either prescriptive diet, prescriptive physical activity, prescriptive diet, and/or physical activity or non-prescriptive lifestyle) and included weight or body mass index (weight [kg]/height [m² ]) were eligible. Twenty-nine trials across 34 publications (participants n = 37,407) were included. Intervention resulted in less weight gain compared with controls (-1.15 kg [95% CI -1.50, -0.80 kg] p < 0.001). Subgroup analysis demonstrated greater effectiveness with prescriptive (-1.60 kg [95% CI -2.00, -1.19] p < 0.001) compared with non-prescriptive (-0.81 kg [95% CI 1.10, -0.53] p < 0.001) intervention types. Interventions had greatest impact in healthy weight (18.5-24.9 kg/m² ) (-0.82 kg [95% CI -1.5, -0.50] p < 0.001) or overweight (25.0-29.9 kg/m² ) (-1.48 kg [95% CI -1.85, -1.12] p < 0.001) compared with obese populations (≥30.0 kg/m² ) (-0.56 kg [95% CI -1.40, 0.27] p = 0.19). These findings demonstrate that lifestyle intervention prevents cumulative weight gain in non-obese adults, with future research required to inform cost-effectiveness and implementation feasibility.

Early Engagement is Associated with Better Weight Loss in Emerging Adults

Objectives: Predictors of success among emerging adults (EAs; ages 18-25) within behavioral weight loss (BWL) trials are largely unknown. We examined whether early program engagement predicted overall engagement and weight loss in EAs. Methods: Data were pooled from 2 randomized controlled pilot trials in EAs. Participants (N = 99, 80% female, BMI = 33.7±5.1 kg/m²) received a 3-month BWL intervention. Weight was objectively assessed at 0 and 3 months; engagement was tracked weekly; retention was assessed at 3 months. Results: Greater engagement during the initial 4 weeks of treatment predicted greater weight loss (p = .001). Compared to those who did not engage in all 4 initial weeks, participants meeting this threshold experienced greater overall engagement (9.6 vs 4.2 weeks, p < .001), weight losses (intent-to-treat = -3.8% vs -1.3%, p = .004), and retention (78% vs 53%, p = .012). Conclusions: Early engagement in BWL is associated with better outcomes among EAs. Monitoring engagement in real-time during the initial 4 weeks of treatment may be necessary to intervene effectively. Early engagement did not vary by sex or race; future work should identify characteristics associated with poor early engagement.