Macronutrient Determinants of Obesity, Insulin Resistance and Metabolic Health

Solon-Biet, Samantha M.; Freire, Therese; Brandon, Amanda E.

doi:10.3390/biology10040336

Cited by 23 publications

(21 citation statements)

References 238 publications

(235 reference statements)

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“…However, our result is similar to the Kerala DPP study, which also found that the effect on HbA1c was stronger than on weight [16]. One explanation is that HbA1c changes may be related to dietary changes or changes in physical activity independent of weight loss [43,44].…”

Section: Plos Medicinesupporting

confidence: 87%

Evaluation of an adapted version of the Diabetes Prevention Program for low- and middle-income countries: A cluster randomized trial to evaluate “Lifestyle Africa” in South Africa

et al. 2022

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Background Low- and middle-income countries (LMICs) are experiencing major increases in diabetes and cardiovascular conditions linked to overweight and obesity. Lifestyle interventions such as the United States National Diabetes Prevention Program (DPP) developed in high-income countries require adaptation and cultural tailoring for LMICs. The objective of this study was to evaluate the efficacy of Lifestyle Africa, an adapted version of the DPP tailored for an underresourced community in South Africa compared to usual care. Methods and findings Participants were residents of a predominantly Xhosa-speaking urban township of Cape Town, South Africa characterized by high rates of poverty. Participants with body mass index (BMI) ≥ 25 kg/m2 who were members of existing social support groups or “clubs” receiving health services from local nongovernmental organizations (NGOs) were enrolled in a cluster randomized controlled trial that compared Lifestyle Africa (the intervention condition) to usual care (the control condition). The Lifestyle Africa intervention consisted of 17 video-based group sessions delivered by trained community health workers (CHWs). Clusters were randomized using a numbered list of the CHWs and their assigned clubs based on a computer-based random allocation scheme. CHWs, participants, and research team members could not be blinded to condition. Percentage weight loss (primary outcome), hemoglobin A1c (HbA1c), blood pressure, triglycerides, and low-density lipoprotein (LDL) cholesterol were assessed 7 to 9 months after enrollment. An individual-level intention-to-treat analysis was conducted adjusting for clustering within clubs and baseline values. Trial registration is at ClinicalTrials.gov (NCT03342274). Between February 2018 and May 2019, 782 individuals were screened, and 494 were enrolled. Participants were predominantly retired (57% were receiving a pension) and female (89%) with a mean age of 68 years. Participants from 28 clusters were allocated to Lifestyle Africa (15, n = 240) or usual care (13, n = 254). Fidelity assessments indicated that the intervention was generally delivered as intended. The modal number of sessions held across all clubs was 17, and the mean attendance of participants across all sessions was 61%. Outcome assessment was completed by 215 (90%) intervention and 223 (88%) control participants. Intent-to-treat analyses utilizing multilevel modeling included all randomized participants. Mean weight change (primary outcome) was −0.61% (95% confidence interval (CI) = −1.22, −0.01) in Lifestyle Africa and −0.44% (95% CI = −1.06, 0.18) in control with no significant difference (group difference = −0.17%; 95% CI = −1.04, 0.71; p = 0.71). However, HbA1c was significantly lower at follow-up in Lifestyle Africa compared to the usual care group (mean difference = −0.24, 95% CI = −0.39, −0.09, p = 0.001). None of the other secondary outcomes differed at follow-up: systolic blood pressure (group difference = −1.36; 95% CI = −6.92, 4.21; p = 0.63), diastolic blood pressure (group difference = −0.39; 95% CI = −3.25, 2.30; p = 0.78), LDL (group difference = −0.07; 95% CI = −0.19, 0.05; p = 0.26), triglycerides (group difference = −0.02; 95% CI = −0.20, 0.16; p = 0.80). There were no unanticipated problems and serious adverse events were rare, unrelated to the intervention, and similar across groups (11 in Lifestyle Africa versus 13 in usual care). Limitations of the study include the lack of a rigorous dietary intake measure and the high representation of older women. Conclusions In this study, we found that Lifestyle Africa was feasible for CHWs to deliver and, although it had no effect on the primary outcome of weight loss or secondary outcomes of blood pressure or triglycerides, it had an apparent small significant effect on HbA1c. The study demonstrates the potential feasibility of CHWs to deliver a program without expert involvement by utilizing video-based sessions. The intervention may hold promise for addressing cardiovascular disease (CVD) and diabetes at scale in LMICs. Trial registration ClinicalTrials.gov NCT03342274.

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Section: Plos Medicinesupporting

confidence: 87%

Evaluation of an adapted version of the Diabetes Prevention Program for low- and middle-income countries: A cluster randomized trial to evaluate “Lifestyle Africa” in South Africa

et al. 2022

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“…Several prospective investigations have studied the relationship between the dietary glycemic index and the development of type II diabetes, a common consequence of insulin resistance [ 40 ]. Specifically, Villegas et al studied a cohort of over 64,000 Chinese women with no history of diabetes or other chronic disease for almost five years [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Macronutrient Intake and Insulin Resistance in 5665 Randomly Selected, Non-Diabetic U.S. Adults

Tucker

2022

Nutrients

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The main goal of this investigation was to evaluate the relationships between several macronutrients and insulin resistance in 5665 non-diabetic U.S. adults. A secondary objective was to determine the extent to which the associations were influenced by multiple potential confounding variables. A cross-sectional design and 8 years of data from the 2011–2018 National Health and Nutrition Examination Survey (NHANES) were used to answer the research questions. Ten macronutrients were evaluated: total carbohydrate, starch, simple carbohydrate, dietary fiber, total protein, total fat, saturated, polyunsaturated, monounsaturated, and total unsaturated fat. The homeostatic model assessment (HOMA), based on fasting glucose and fasting insulin levels, was used to index insulin resistance. Age, sex, race, year of assessment, physical activity, cigarette smoking, alcohol use, and waist circumference were used as covariates. The relationships between total carbohydrate intake (F = 6.7, p = 0.0121), simple carbohydrate (F = 4.7, p = 0.0344) and HOMA-IR were linear and direct. The associations between fiber intake (F = 9.1, p = 0.0037), total protein (F = 4.4, p = 0.0393), total fat (F = 5.5, p = 0.0225), monounsaturated fat (F = 5.5, p = 0.0224), and total unsaturated fat (F = 6.5, p = 0.0132) were linear and inversely related to HOMA-IR, with 62 degrees of freedom. Starch, polyunsaturated fat, and saturated fat intakes were not related to HOMA-IR. In conclusion, in this nationally representative sample, several macronutrients were significant predictors of insulin resistance in U.S. adults.

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“…As demonstrated by‐different prospective studies, waist/height ratio rather than BMI and WHR is an accurate index for predicting dyslipidemia, hypertension, and MetS 48,49 . Despite several simple methods available to assess abdominal adiposity, proactive management of this disorder at an early stage is of serious concern 50–52 . It has been shown from epidemiological and experimental studies that physical activity/exercise could induce mobilization of visceral fat and reduce central adiposity 53 .…”

Section: Pathophysiology Of Metsmentioning

confidence: 99%

Dietary carbohydrates: Pathogenesis and potential therapeutic targets to obesity‐associated metabolic syndrome

Akter

Akhter

Chaudhury³

et al. 2022

BioFactors

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Metabolic syndrome (MetS) is a common feature in obesity, comprising a cluster of abnormalities including abdominal fat accumulation, hyperglycemia, hyperinsulinemia, dyslipidemia, and hypertension, leading to diabetes and cardiovascular diseases (CVD). Intake of carbohydrates (CHO), particularly a sugary diet that rapidly increases blood glucose, triglycerides, and blood pressure levels is the predominant determining factor of MetS. Complex CHO, on the other hand, are a stable source of energy taking a longer time to digest. In particular, resistant starch (RS) or soluble fiber is an excellent source of prebiotics, which alter the gut microbial composition, which in turn improves metabolic control. Altering maternal CHO intake during pregnancy may result in the child developing MetS. Furthermore, lifestyle factors such as physical inactivity in combination with dietary habits may synergistically influence gene expression by modulating genetic and epigenetic regulators transforming childhood obesity into adolescent metabolic disorders. This review summarizes the common pathophysiology of MetS in connection with the nature of CHO, intrauterine nutrition, genetic predisposition, lifestyle factors, and advanced treatment approaches; it also emphasizes how dietary CHO may act as a key element in the pathogenesis and future therapeutic targets of obesity and MetS.

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Macronutrient Determinants of Obesity, Insulin Resistance and Metabolic Health

Cited by 23 publications

References 238 publications

Evaluation of an adapted version of the Diabetes Prevention Program for low- and middle-income countries: A cluster randomized trial to evaluate “Lifestyle Africa” in South Africa

Evaluation of an adapted version of the Diabetes Prevention Program for low- and middle-income countries: A cluster randomized trial to evaluate “Lifestyle Africa” in South Africa

Macronutrient Intake and Insulin Resistance in 5665 Randomly Selected, Non-Diabetic U.S. Adults

Dietary carbohydrates: Pathogenesis and potential therapeutic targets to obesity‐associated metabolic syndrome

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