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1.
Metabolic Syndrome, Cognitive Impairment and the Role of Diet: A Narrative Review.
Kouvari, M, D'Cunha, NM, Travica, N, Sergi, D, Zec, M, Marx, W, Naumovski, N
Nutrients. 2022;(2)
Abstract
BACKGROUND This narrative review presents the association between metabolic syndrome (MetS), along with its components, and cognition-related disorders, as well as the potential reversal role of diet against cognitive impairment by modulating MetS. METHODS An electronic research in Medline (Pubmed) and Scopus was conducted. RESULTS MetS and cognitive decline share common cardiometabolic pathways as MetS components can trigger cognitive impairment. On the other side, the risk factors for both MetS and cognitive impairment can be reduced by optimizing the nutritional intake. Clinical manifestations such as dyslipidemia, hypertension, diabetes and increased central body adiposity are nutrition-related risk factors present during the prodromal period before cognitive impairment. The Mediterranean dietary pattern stands among the most discussed predominantly plant-based diets in relation to cardiometabolic disorders that may prevent dementia, Alzheimer's disease and other cognition-related disorders. In addition, accumulating evidence suggests that the consumption of specific dietary food groups as a part of the overall diet can improve cognitive outcomes, maybe due to their involvement in cardiometabolic paths. CONCLUSIONS Early MetS detection may be helpful to prevent or delay cognitive decline. Moreover, this review highlights the importance of healthy nutritional habits to reverse such conditions and the urgency of early lifestyle interventions.
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2.
Global, regional, and country estimates of metabolic syndrome burden in children and adolescents in 2020: a systematic review and modelling analysis.
Noubiap, JJ, Nansseu, JR, Lontchi-Yimagou, E, Nkeck, JR, Nyaga, UF, Ngouo, AT, Tounouga, DN, Tianyi, FL, Foka, AJ, Ndoadoumgue, AL, et al
The Lancet. Child & adolescent health. 2022;(3):158-170
Abstract
BACKGROUND Halting the rise in cardiometabolic risk factors in children and adolescents is crucial to curb the global burden of cardiovascular diseases. We aim to provide global, regional, and national estimates of the prevalence of metabolic syndrome in children and adolescents to support the development of evidence-based prevention strategies. METHODS In this systematic review with modelling analysis, we searched PubMed, Embase, Africa Journal Online, and Global Index Medicus from database inception to Jan 30, 2021, with no restriction on language or geographical location. We included community-based and school-based cross-sectional studies and cross-sectional analysis of cohort studies that reported prevalence of metabolic syndrome in the general population of children (6-12 years) and adolescents (13-18 years). Only studies with a low risk of bias were considered. Eligible studies included at least 200 participants and used probabilistic-based sampling. Diagnosis of metabolic syndrome had to meet at least three of the following criteria: high systolic or diastolic blood pressure (≥90th percentile for age, sex, and height); waist circumference in at least the 90th percentile for age, sex, and ethnic group; fasting plasma glucose 5·6 mmol/L or greater; fasting plasma triglycerides 1·24 mmol/L or greater; and fasting plasma high density lipoprotein cholesterol 1·03 mmol/L or less. Independent investigators selected eligible studies and extracted relevant data. The primary outcome was a crude estimate of metabolic syndrome prevalence, assessed using a Bayesian hierarchical model. FINDINGS Our search yielded 6808 items, of which 169 studies were eligible for analysis, including 306 prevalence datapoints, with 550 405 children and adolescents from 44 countries in 13 regions. The between-study variance (τ2) was 0·52 (95% CI 0·42-0·67), which could reflect the measurement of each component of the metabolic syndrome and covariates as sources of between-study heterogeneity. We estimated the global prevalence of metabolic syndrome in 2020 at 2·8% (95% uncertainty interval [UI] 1·4-6·7) for children and 4·8% (2·9-8·5) for adolescents, equating to around 25·8 (12·6-61·0) million children and 35·5 (21·3-63·0) million adolescents living with metabolic syndrome. In children, the prevalence of metabolic syndrome was 2·2% (95% UI 1·4-3·6) in high-income countries, 3·1% (2·5-4·3) in upper-middle-income countries, 2·6% (0·9-8·3) in lower-middle-income countries, and 3·5% (1·0-8·0) in low-income countries. In adolescents, the prevalence of metabolic syndrome was 5·5% (4·1-8·4) in high-income countries, 3·9% (3·1-5·4) in upper-middle-income countries, 4·5% (2·6-8·4) in lower-middle-income countries, and 7·0% (2·4-15·7) in low-income countries. Prevalence in children varied from 1·4% (0·6-3·1) in northwestern Europe to 8·2% (6·9-10·1) in Central Latin America. Prevalence for adolescents ranged from 2·9% (95% UI 2·6-3·3) in east Asia to 6·7% (5·9-8·3) in high-income English-speaking countries. The three countries with the highest prevalence estimates in children were Nicaragua (5·2%, 2·8-10·4), Iran (8·8%, 8·0-9·6), and Mexico (12·3%, 11·0-13·7); and the three countries with the highest prevalence estimates in adolescents were Iran (9·0%, 8·4-9·7), United Arab Emirates (9·8%, 8·5-10·3), and Spain (9·9%, 9·1-10·8). INTERPRETATION In 2020, about 3% of children and 5% of adolescents had metabolic syndrome, with some variation across countries and regions. The prevalence of metabolic syndrome was not consistently higher with increasing level of development, suggesting that the problem is not mainly driven by country wealth. The high number of children and adolescents living with metabolic syndrome globally highlights the urgent need for multisectoral interventions to reduce the global burden of metabolic syndrome and the conditions that lead to it, including childhood overweight and obesity. FUNDING None.
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3.
Blueberry anthocyanin intake attenuates the postprandial cardiometabolic effect of an energy-dense food challenge: Results from a double blind, randomized controlled trial in metabolic syndrome participants.
Curtis, PJ, Berends, L, van der Velpen, V, Jennings, A, Haag, L, Chandra, P, Kay, CD, Rimm, EB, Cassidy, A
Clinical nutrition (Edinburgh, Scotland). 2022;(1):165-176
Abstract
BACKGROUND & AIMS Whilst the cardioprotective effects of blueberry intake have been shown in prospective studies and short-term randomized controlled trials (RCTs), it is unknown whether anthocyanin-rich blueberries can attenuate the postprandial, cardiometabolic dysfunction which follows energy-dense food intakes; especially in at-risk populations. We therefore examined whether adding blueberries to a high-fat/high-sugar meal affected the postprandial cardiometabolic response over 24 h. METHODS A parallel, double-blind RCT (n = 45; age 63.4 ± 7.4 years; 64% male; BMI 31.4 ± 3.1 kg/m2) was conducted in participants with metabolic syndrome. After baseline assessments, an energy-dense drink (969 Kcals, 64.5 g fat, 84.5 g carbohydrate, 17.9 g protein) was consumed with either 26 g (freeze-dried) blueberries (equivalent to 1 cup/150 g fresh blueberries) or 26 g isocaloric matched placebo. Repeat blood samples (30, 60, 90, 120, 180, 360 min and 24 h), a 24 h urine collection and vascular measures (at 3, 6, and 24 h) were performed. Insulin and glucose, lipoprotein levels, endothelial function (flow mediated dilatation (FMD)), aortic and systemic arterial stiffness (pulse wave velocity (PWV), Augmentation Index (AIx) respectively), blood pressure (BP), and anthocyanin metabolism (serum and 24 h urine) were assessed. RESULTS Blueberries favorably affected postprandial (0-24 h) concentrations of glucose (p < 0.001), insulin (p < 0.01), total cholesterol (p = 0.04), HDL-C, large HDL particles (L-HDL-P) (both p < 0.01), extra-large HDL particles (XL-HDL-P; p = 0.04) and Apo-A1 (p = 0.01), but not LDL-C, TG, or Apo-B. After a transient higher peak glucose concentration at 1 h after blueberry intake ([8.2 mmol/L, 95%CI: 7.7, 8.8] vs placebo [6.9 mmol/L, 95%CI: 6.4, 7.4]; p = 0.001), blueberries significantly attenuated 3 h glucose ([4.3 mmol/L, 95%CI: 3.8, 4.8] vs placebo [5.1 mmol/L, 95%CI: 4.6, 5.6]; p = 0.03) and insulin concentrations (blueberry: [23.4 pmol/L, 95%CI: 15.4, 31.3] vs placebo [52.9 pmol/L, 95%CI: 41.0, 64.8]; p = 0.0001). Blueberries also improved HDL-C ([1.12 mmol/L, 95%CI: 1.06, 1.19] vs placebo [1.08 mmol/L, 95%CI: 1.02, 1.14]; p = 0.04) at 90 min and XL-HDLP levels ([0.38 × 10-6, 95%CI: 0.35, 0.42] vs placebo [0.35 × 10-6, 95%CI: 0.32, 0.39]; p = 0.02) at 3 h. Likewise, significant improvements were observed 6 h after blueberries for HDL-C ([1.17 mmol/L, 95%CI: 1.11, 1.24] vs placebo [1.10 mmol/L, 95%CI: 1.03, 1.16]; p < 0.001), Apo-A1 ([1.37 mmol/L, 95%CI: 1.32, 1.41] vs placebo [1.31 mmol/L, 95%CI: 1.27, 1.35]; p = 0.003), L-HDLP ([0.70 × 10-6, 95%CI: 0.60, 0.81] vs placebo [0.59 × 10-6, 95%CI: 0.50, 0.68]; p = 0.003) and XL-HDLP ([0.44 × 10-6, 95%CI: 0.40, 0.48] vs placebo [0.40 × 10-6, 95%CI: 0.36, 0.44]; p < 0.001). Similarly, total cholesterol levels were significantly lower 24 h after blueberries ([4.9 mmol/L, 95%CI: 4.6, 5.1] vs placebo [5.0 mmol/L, 95%CI: 4.8, 5.3]; p = 0.04). Conversely, no effects were observed for FMD, PWV, AIx and BP. As anticipated, total anthocyanin-derived phenolic acid metabolite concentrations significantly increased in the 24 h after blueberry intake; especially hippuric acid (6-7-fold serum increase, 10-fold urinary increase). In exploratory analysis, a range of serum/urine metabolites were associated with favorable changes in total cholesterol, HDL-C, XL-HDLP and Apo-A1 (R = 0.43 to 0.50). CONCLUSIONS For the first time, in an at-risk population, we show that single-exposure to the equivalent of 1 cup blueberries (provided as freeze-dried powder) attenuates the deleterious postprandial effects of consuming an energy-dense high-fat/high-sugar meal over 24 h; reducing insulinaemia and glucose levels, lowering cholesterol, and improving HDL-C, fractions of HDL-P and Apo-A1. Consequently, intake of anthocyanin-rich blueberries may reduce the acute cardiometabolic burden of energy-dense meals. CLINICAL TRIAL REGISTRY NCT02035592 at www.clinicaltrials.gov.
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4.
Impact of synbiotic supplementation on cardiometabolic and anthropometric indices in patients with metabolic syndrome: A systematic review and meta-analysis of randomized controlled trials.
Arabi, SM, Bahrami, LS, Rahnama, I, Sahebkar, A
Pharmacological research. 2022;:106061
Abstract
BACKGROUND Probiotic and synbiotic products are being widely used by a large number of patients and clinicians; however, effects on cardiometabolic indices in patients with the metabolic syndrome remain unclear. This meta-analysis aimed to evaluate the effects of a synbiotic intervention on lipid profile, insulin resistance, blood pressure, anthropometric parameters, and inflammatory markers. METHODS We searched MEDLINE, Scopus, and Clarivate Analytics Web of Science by October 2021. Studies were selected if they reported the effectiveness of the synbiotic intervention on cardiometabolic and anthropometric indices. The weighted mean difference was calculated as the effect size using a random-effects model. Subgroup analyses were conducted to determine sources of heterogeneity. Dose-dependent effects were assessed using a dose-response meta-analysis of differences in means. RESULTS Five trials (1049 participants) were finally included in the meta-analysis. Synbiotic intervention significantly reduced serum insulin levels (WMD, -6.39 μU/mL; 95%CI, (-7.2 to -5.4); p = 0.001, I2 = 88.2%, N = 5), triglycerides (WMD, -20.3 mg/dl; 95%CI, (-32.7 to -7.8); p = 0.001, I2 = 87.7, N = 5), total cholesterol (WMD, -7.8 mg/dl; 95%CI, ( -12.5 to -3.02); p = 0.001; I2 = 66.7%, N = 5), low-density lipoprotein cholesterol (WMD, -9.02 mg/dl; 95%CI, (-10.8 to -7.2); p < 0.001, I2 = 0%, N = 5), waist circumference (WMD, -4.04 cm; 95%CI, ( -4.9 to -3.08), p < 0.001; I2 = 22.7%, N = 3), body weight (WMD, -4.3 kg; 95%CI, (-6.2 to -2.5); p = 0.001; I2 = 0%, N = 2), systolic blood pressure (WMD, -1.8 mmHg; 95% CI, (-2.8 to -0.7); p = 0.001; I2 = 0%, N = 3), and serum interleukin-6 concentrations (WMD, -0.2 pg/mL; 95%CI, (-0.3 to -0.08); p = 0.001, I2 = 39.8%, N = 2), and increased high-density lipoprotein cholesterol levels (WMD, 2.3 mg/dl; 95%CI, (0.2-4.4); p = 0.03; 03; I2 = 93.1%, N = 5). Synbiotic administration did not significantly affect fasting plasma glucose, homeostatic model assessment for insulin resistance, body mass index, diastolic blood pressure, heart rate, and serum C-reactive protein concentrations. CONCLUSIONS The present findings suggest that synbiotic intervention effectively improves cardiometabolic risk factors in patients with metabolic syndrome.
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5.
Extreme Birth Weight and Metabolic Syndrome in Children.
Bizerea-Moga, TO, Pitulice, L, Pantea, CL, Olah, O, Marginean, O, Moga, TV
Nutrients. 2022;(1)
Abstract
Small and large birth weights (BWs) for gestational age (GA) represent extremes, but the correlation between extreme BW and metabolic syndrome (MetS) has not been fully elucidated. In this study, we examined this correlation in obese children based on changes in their metabolic profile from childhood to adolescence. A retrospective observational study was performed on 535 obese patients aged 0-18 years in the Clinical and Emergency Hospital for Children "Louis Turcanu" in Timisoara, Romania, based on clinical and biological data from January 2015 to December 2019. We emphasized the links between extreme BW and obesity, extreme BW and cardiometabolic risk, obesity and cardiometabolic risk, and extreme BW, obesity and MetS. Children born large for gestational age (LGA) predominated over those born small for gestational age (SGA). Our findings showed that BW has an independent effect on triglycerides and insulin resistance, whereas obesity had a direct influence on hypertension, impaired glucose metabolism and hypertriglyceridemia. The influences of BW and obesity on the development of MetS and its components are difficult to separate; therefore, large prospective studies in normal-weight patients are needed.
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6.
Mechanistic Targets and Nutritionally Relevant Intervention Strategies to Break Obesity-Breast Cancer Links.
Bustamante-Marin, XM, Merlino, JL, Devericks, E, Carson, MS, Hursting, SD, Stewart, DA
Frontiers in endocrinology. 2021;:632284
Abstract
The worldwide prevalence of overweight and obesity has tripled since 1975. In the United States, the percentage of adults who are obese exceeds 42.5%. Individuals with obesity often display multiple metabolic perturbations, such as insulin resistance and persistent inflammation, which can suppress the immune system. These alterations in homeostatic mechanisms underlie the clinical parameters of metabolic syndrome, an established risk factor for many cancers, including breast cancer. Within the growth-promoting, proinflammatory milieu of the obese state, crosstalk between adipocytes, immune cells and breast epithelial cells occurs via obesity-associated hormones, angiogenic factors, cytokines, and other mediators that can enhance breast cancer risk and/or progression. This review synthesizes evidence on the biological mechanisms underlying obesity-breast cancer links, with emphasis on emerging mechanism-based interventions in the context of nutrition, using modifiable elements of diet alone or paired with physical activity, to reduce the burden of obesity on breast cancer.
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7.
Triglyceride/High-Density Lipoprotein Cholesterol Ratio: A Clue to Metabolic Syndrome, Insulin Resistance, and Severe Atherosclerosis.
Azarpazhooh, MR, Najafi, F, Darbandi, M, Kiarasi, S, Oduyemi, T, Spence, JD
Lipids. 2021;(4):405-412
Abstract
High serum levels of triglycerides (Tg) and low levels of high-density lipoprotein cholesterol (HDL-C) are characteristic of the Metabolic Syndrome (MetS). We assessed the ratio of Tg to HDL-C as a way to identify MetS and insulin resistance. We also evaluated its association with severity of carotid atherosclerosis. Data were analyzed from three cohorts totaling 13,908 participants. MetS was defined according to the International Diabetes Federation criteria. Optimal cut-off for Tg/HDL-C ratio was obtained using Youden's index in receiver-operating characteristic (ROC) curve analyses. The risk of MetS and IR in those with a Tg/HDL-C ratio above the optimum cutoff was evaluated by logistic regression analysis. A Tg/HDL-C ratio above the optimal cutoff level significantly increased the odds ratio for MetS in the three cohorts (OR 6.00, 4.04, and 3.50, least in the healthy population), identified insulin resistance defined by the homeostatic model of insulin resistance (HOMA-IR) (p < 0.0001), and was strongly associated with atherosclerosis severity (p = 0.0001). Tg/HDL-C ratio identifies persons with MetS, insulin resistance, and severe atherosclerosis. It should be used more widely to identify patients at high risk. This is clinically important because insulin resistance is treatable.
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8.
Efficacy of IVRS-based mHealth intervention in reducing cardiovascular risk in metabolic syndrome: A cluster randomized trial.
Sharma, AK, Baig, VN, Ahuja, J, Sharma, S, Panwar, RB, Katoch, VM, Gupta, R
Diabetes & metabolic syndrome. 2021;(5):102182
Abstract
AIMS: Efficacy of mobile-phone based intervention for reducing cardiovascular risk in metabolic syndrome (MetSyn). METHODS We screened adults 20-60 years in 10 villages in India for MetSyn using stratified cluster sampling. Lifestyle and biochemical risk factors were assessed. International Harmonized Criteria were used for diagnosis. Villages were randomized with 5 each in control and intervention groups. Interactive voice response system (IVRS) in Hindi was developed. In intervention clusters two messages for promotion of healthy lifestyle and medical treatment were broadcast daily over 12-months and risk factors reassessed. RESULTS 1012/1200(84%) persons were screened and MetSyn diagnosed in 286(28.3%). Villages were divided into 5 control(n = 136) and 5 intervention(n = 147) clusters. Baseline characteristics in both clusters were similar. Acceptability of intervention was >60% in 80% participants. At 12 months, significantly greater participants in intervention vs control clusters had healthier lifestyle (healthy diet 28.8vs14.7%, physical activity 25.9vs13.1%, tobacco 13.7vs32.5%), anthropometry (waist circumference 85.7 ± 6.3vs88.6 ± 14.0 cm, body mass index 21.9 ± 2.8vs23.1 ± 2.9 kg/m2), systolic BP 123.6 ± 7.7vs128.6 ± 14.1 mmHg, fasting glucose 95.6 ± 19.4vs109.4 ± 43.7 mg/dl, cholesterol 175.5 ± 36.5vs186.4 ± 43.3 mg/dl, and triglycerides 147.6 ± 48.3vs159.5 ± 60.7 mg/dl (p < 0.01). Prevalence of metabolic syndrome declined in intervention group by 22.3%vs3.9%, p < 0.001). CONCLUSION An interactive voice response system based technology significantly reduced multiple cardiovascular risk factors and prevalence of metabolic syndrome.
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9.
The Effects of Shift Work on Cardio-Metabolic Diseases and Eating Patterns.
Hemmer, A, Mareschal, J, Dibner, C, Pralong, JA, Dorribo, V, Perrig, S, Genton, L, Pichard, C, Collet, TH
Nutrients. 2021;(11)
Abstract
Energy metabolism is tightly linked with circadian rhythms, exposure to ambient light, sleep/wake, fasting/eating, and rest/activity cycles. External factors, such as shift work, lead to a disruption of these rhythms, often called circadian misalignment. Circadian misalignment has an impact on some physiological markers. However, these proxy measurements do not immediately translate into major clinical health outcomes, as shown by later detrimental health effects of shift work and cardio-metabolic disorders. This review focuses on the effects of shift work on circadian rhythms and its implications in cardio-metabolic disorders and eating patterns. Shift work appears to be a risk factor of overweight, obesity, type 2 diabetes, elevated blood pressure, and the metabolic syndrome. However, past studies showed discordant findings regarding the changes of lipid profile and eating patterns. Most studies were either small and short lab studies, or bigger and longer cohort studies, which could not measure health outcomes in a detailed manner. These two designs explain the heterogeneity of shift schedules, occupations, sample size, and methods across studies. Given the burden of non-communicable diseases and the growing concerns about shift workers' health, novel approaches to study shift work in real contexts are needed and would allow a better understanding of the interlocked risk factors and potential mechanisms involved in the onset of metabolic disorders.
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10.
High prevalence of fragmented QRS on electrocardiography in Japanese patients with diabetes irrespective of metabolic syndrome.
Yagi, K, Nagata, Y, Yamagami, T, Chujo, D, Kamigishi, M, Yokoyama-Nakagawa, M, Shikata, M, Enkaku, A, Takikawa-Nishida, A, Honoki, H, et al
Journal of diabetes investigation. 2021;(9):1680-1688
Abstract
AIMS/INTRODUCTION Fragmented QRS (fQRS) on electrocardiography is a marker of myocardial fibrosis and myocardial scar formation. This study aimed to clarify the relationship of fQRS with diabetes mellitus and metabolic syndrome (MetS) in Japanese patients. MATERIALS AND METHODS Approximately 702 individuals who had a routine health checkup at the Hokuriku Health Service Association (Toyama, Japan) in October 2014 were enrolled and categorized into one of the following four groups based on MetS and diabetes mellitus status: with diabetes mellitus (+) MetS+ (164 participants); diabetes mellitus+ without MetS (Mets-; 103 participants); diabetes mellitus- MetS+ (133 participants); and diabetes mellitus- MetS- (302 participants). fQRS was assessed using the results of electrocardiography. RESULTS The prevalence of fQRS was statistically higher in patients with diabetes mellitus+ MetS+ (37%) and diabetes mellitus+ MetS- (35%), than those with diabetes mellitus- MetS+ (14%) or diabetes mellitus- MetS- (10%; P < 0.0001). Significant differences were observed between the fQRS(+) and fQRS(-) groups for age, sex, waist circumference, heart rate, hypertension, hemoglobin A1c, total cholesterol, MetS and diabetes mellitus. The area under the receiver operating characteristic curve for traditional risk factors and diabetes mellitus was 0.72 (P = 0.0007, 95% confidence interval 0.67-0.76), and for traditional risk factors and MetS it was 0.67 (P = 0.28, 95% confidence interval 0.62-0.72). Patients with diabetes mellitus had more than threefold higher likelihood of showing fQRS (odds ratio 3.41; 95% confidence interval 2.25-5.22; P < 0.0001) compared with the reference group without diabetes mellitus, after adjusting for age, sex, dyslipidemia, hypertension and waist circumference. CONCLUSIONS fQRS was observed more frequently in diabetes mellitus patients than in MetS and control individuals. Diabetes mellitus was the most significant determinant for fQRS among MetS and other traditional metabolic risk factors.