METHODS: A total of 20 healthy volunteers were challenged with 3 test meals, similar in fat content (~31% en) but varying in saturated SFA content and polyunsaturated/saturated fatty acid ratios (P/S). The 3 meals were lauric + myristic acid-rich (LM), P/S 0.19; palmitic acid-rich (POL), P/S 0.31; and stearic acid-rich (STE), P/S 0.22. Blood was sampled at fasted baseline and 2, 4, 5, 6, and 8 hours. Plasma lipids (triacylglycerol [TAG]) and lipoproteins (TC, LDL-C, high density lipoprotein-cholesterol [HDL-C]) were evaluated.

RESULTS: Varying SFA in the test meal significantly impacted postprandial TAG response (p < 0.05). Plasma TAG peaked at 5 hours for STE, 4 hours for POL, and 2 hours for LM test meals. Area-under-the-curve (AUC) for plasma TAG was increased significantly after STE treatment (STE > LM by 32.2%, p = 0.003; STE > POL by 27.9%, p = 0.023) but was not significantly different between POL and LM (POL > LM by 6.0%, p > 0.05). At 2 hours, plasma HDL-C increased significantly after the LM and POL test meals compared with STE (p < 0.05). In comparison to the STE test meal, HDL-C AUC was elevated 14.0% (p = 0.005) and 7.6% (p = 0.023) by the LM and POL test meals, respectively. The TC response was also increased significantly by LM compared with both POL and STE test meals (p < 0.05).

AIM: To investigate the effects of food order on postprandial glucose (PPG) excursion, in Indian adults with normal (NL) and overweight/obese (OW) Body Mass Index.

METHODS: This randomised crossover study was conducted at a Malaysian university among Indian adults without diabetes. The participants consumed isocaloric test meals at three study visits based on randomised food orders: carbohydrate first/protein last (CF); protein first/carbohydrate last (CL); and a composite meal containing carbohydrate and protein (CM). Capillary blood glucose was measured at baseline, 30, 60, 90 and 120 minutes after starting the meal.

RESULTS: The CL food order had a blunting effect on PPG excursion at 30 and 60 minutes (p < 0.01). The CL food order resulted in lower glucose peak when compared with the CF and CM food order (p < 0.001). The CL food order resulted in lower incremental glucose peak (mmol/L) (NL: CF 3.9 ± 0.3, CM 3.0 ± 0.3, CL 2.0 ± 0.2; OW: CF 2.9 ± 0.3, CM 2.5 ± 0.3, CL 1.8 ± 0.2) and iAUC 0-120 min (mmol/Lxmin) (NL: CF 272.4 ± 26.7, CM 206.2 ± 30.3, CL 122.0 ± 14.8; OW: CF 193.2 ± 23.1, CM 160.1 ± 21.7, CL 113.6 ± 15.3) when compared with the CF food order (p < 0.001). The effect of food order on postprandial excursion did not differ between the NL (n = 14) and the OW (n = 17) groups.

METHODS: This human postprandial study evaluated 3 edible fat blends with differing polyunsaturated to saturated fatty acids (P/S) ratios (POL = 0.27, AHA = 1.00, PCAN = 1.32). A cross-over design included mildly hypercholestrolemic subjects (9 men and 6 women) preconditioned on test diets fats at 31% energy for 7 days prior to the postprandial challenge on the 8th day with 50 g test fat. Plasma lipids and lipoproteins were monitored at 0, 1.5, 3.5, 5.5 and 7 hr.

Objective: This review aims to summarize the clinical evidence regarding the use of chia seed for a wide variety of health conditions.

Data Sources: A number of databases, including PubMed and Embase, were searched systematically.

Study Selection: Randomized controlled trials that assessed the clinical effects of chia seed consumption in human participants were included. The quality of trials was assessed using the Cochrane Risk of Bias Tool.

Data Extraction: Data on study design, blinding status, characteristics of participants, chia seed intervention, comparator, clinical assessment, duration of intake, interval of assessment, and study funding status were extracted. Meta-analysis was performed.

Results: Twelve trials were included. Participants included healthy persons, athletes, diabetic patients, and individuals with metabolic syndrome. Pooling of results showed no significant differences except for the following findings of subgroup analysis at higher doses of chia seed: (1) lower postprandial blood glucose level (mean difference [MD] of -33.95 incremental area under the curve [iAUC] [mmol/L × 2 h] [95%CI, -61.85, -6.05] and -51.60 iAUC [mmol/L × 2 h] [95%CI, -79.64, -23.56] at medium doses and high doses, respectively); (2) lower high-density lipoprotein in serum (MD of -0.10 mmol/L [95%CI, -0.20, -0.01]); and (3) lower diastolic blood pressure (MD of -7.14 mmHg [95%CI, -11.08, -3.19]). The quality of all evidence assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was low or very low. All trials employed only surrogate markers as outcomes.

Conclusions: Future trials with improved methodological quality, well-described clinical events, and validated surrogate markers as outcomes are needed to support the potential health benefits of chia seed consumption.

OBJECTIVE: We investigated the effects of high-protein Malaysian diets prepared with palm olein, coconut oil (CO), or virgin olive oil on plasma homocysteine and selected markers of inflammation and cardiovascular disease (CVD) in healthy adults.

DESIGN: A randomized-crossover intervention with 3 dietary sequences of 5 wk each was conducted in 45 healthy subjects. The 3 test fats, namely palmitic acid (16:0)-rich palm olein (PO), lauric and myristic acid (12:0 + 14:0)-rich CO, and oleic acid (18:1)-rich virgin olive oil (OO), were incorporated at two-thirds of 30% fat calories into high-protein Malaysian diets.

RESULTS: No significant differences were observed in the effects of the 3 diets on plasma total homocysteine (tHcy) and the inflammatory markers TNF-α, IL-1β, IL-6, and IL-8, high-sensitivity C-reactive protein, and interferon-γ. Diets prepared with PO and OO had comparable nonhypercholesterolemic effects; the postprandial total cholesterol for both diets and all fasting lipid indexes for the OO diet were significantly lower (P < 0.05) than for the CO diet. Unlike the PO and OO diets, the CO diet was shown to decrease postprandial lipoprotein(a).

OBJECTIVES: To compare techniques of blood glucose monitoring and their impact on maternal and infant outcomes among pregnant women with pre-existing diabetes.

SEARCH METHODS: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (30 November 2016), searched reference lists of retrieved studies and contacted trial authors.

SELECTION CRITERIA: Randomised controlled trials (RCTs) and quasi-RCTs comparing techniques of blood glucose monitoring including SMBG, continuous glucose monitoring (CGM) or clinic monitoring among pregnant women with pre-existing diabetes mellitus (type 1 or type 2). Trials investigating timing and frequency of monitoring were also included. RCTs using a cluster-randomised design were eligible for inclusion but none were identified.

DATA COLLECTION AND ANALYSIS: Two review authors independently assessed study eligibility, extracted data and assessed the risk of bias of included studies. Data were checked for accuracy. The quality of the evidence was assessed using the GRADE approach.

MAIN RESULTS: This review update includes at total of 10 trials (538) women (468 women with type 1 diabetes and 70 women with type 2 diabetes). The trials took place in Europe and the USA. Five of the 10 included studies were at moderate risk of bias, four studies were at low to moderate risk of bias, and one study was at high risk of bias. The trials are too small to show differences in important outcomes such as macrosomia, preterm birth, miscarriage or death of baby. Almost all the reported GRADE outcomes were assessed as being very low-quality evidence. This was due to design limitations in the studies, wide confidence intervals, small sample sizes, and few events. In addition, there was high heterogeneity for some outcomes.Various methods of glucose monitoring were compared in the trials. Neither pooled analyses nor individual trial analyses showed any clear advantages of one monitoring technique over another for primary and secondary outcomes. Many important outcomes were not reported.1. Self-monitoring versus standard care (two studies, 43 women): there was no clear difference for caesarean section (risk ratio (RR) 0.78, 95% confidence interval (CI) 0.40 to 1.49; one study, 28 women) or glycaemic control (both very low-quality), and not enough evidence to assess perinatal mortality and neonatal mortality and morbidity composite. Hypertensive disorders of pregnancy, large-for-gestational age, neurosensory disability, and preterm birth were not reported in either study.2. Self-monitoring versus hospitalisation (one study, 100 women): there was no clear difference for hypertensive disorders of pregnancy (pre-eclampsia and hypertension) (RR 4.26, 95% CI 0.52 to 35.16; very low-quality: RR 0.43, 95% CI 0.08 to 2.22; very low-quality). There was no clear difference in caesarean section or preterm birth less than 37 weeks' gestation (both very low quality), and the sample size was too small to assess perinatal mortality (very low-quality). Large-for-gestational age, mortality or morbidity composite, neurosensory disability and preterm birth less than 34 weeks were not reported.3. Pre-prandial versus post-prandial glucose monitoring (one study, 61 women): there was no clear difference between groups for caesarean section (RR 1.45, 95% CI 0.92 to 2.28; very low-quality), large-for-gestational age (RR 1.16, 95% CI 0.73 to 1.85; very low-quality) or glycaemic control (very low-quality). The results for hypertensive disorders of pregnancy: pre-eclampsia and perinatal mortality are not meaningful because these outcomes were too rare to show differences in a small sample (all very low-quality). The study did not report the outcomes mortality or morbidity composite, neurosensory disability or preterm birth.4. Automated telemedicine monitoring versus conventional system (three studies, 84 women): there was no clear difference for caesarean section (RR 0.96, 95% CI 0.62 to 1.48; one study, 32 women; very low-quality), and mortality or morbidity composite in the one study that reported these outcomes. There were no clear differences for glycaemic control (very low-quality). No studies reported hypertensive disorders of pregnancy, large-for-gestational age, perinatal mortality (stillbirth and neonatal mortality), neurosensory disability or preterm birth.5.CGM versus intermittent monitoring (two studies, 225 women): there was no clear difference for pre-eclampsia (RR 1.37, 95% CI 0.52 to 3.59; low-quality), caesarean section (average RR 1.00, 95% CI 0.65 to 1.54; I² = 62%; very low-quality) and large-for-gestational age (average RR 0.89, 95% CI 0.41 to 1.92; I² = 82%; very low-quality). Glycaemic control indicated by mean maternal HbA1c was lower for women in the continuous monitoring group (mean difference (MD) -0.60 %, 95% CI -0.91 to -0.29; one study, 71 women; moderate-quality). There was not enough evidence to assess perinatal mortality and there were no clear differences for preterm birth less than 37 weeks' gestation (low-quality). Mortality or morbidity composite, neurosensory disability and preterm birth less than 34 weeks were not reported.6. Constant CGM versus intermittent CGM (one study, 25 women): there was no clear difference between groups for caesarean section (RR 0.77, 95% CI 0.33 to 1.79; very low-quality), glycaemic control (mean blood glucose in the 3rd trimester) (MD -0.14 mmol/L, 95% CI -2.00 to 1.72; very low-quality) or preterm birth less than 37 weeks' gestation (RR 1.08, 95% CI 0.08 to 15.46; very low-quality). Other primary (hypertensive disorders of pregnancy, large-for-gestational age, perinatal mortality (stillbirth and neonatal mortality), mortality or morbidity composite, and neurosensory disability) or GRADE outcomes (preterm birth less than 34 weeks' gestation) were not reported.