Academic literature on the topic 'Macronutrient - Sleep'

Sleep is involved in metabolic, emotional and cognitive regulation and is therefore an essential part of our health. Although an association between sleep quality and macronutrient intake has been reported, studies on the effect of macronutrient distribution with sleep quality are limited, and available results are inconsistent. In this study, we aim to assess the association between sleep quality and macronutrient distribution in healthy adults from systematically reviewed cross-sectional studies and randomized controlled trials (RCTs). A total of 19 relevant articles were selected and it was observed that good sleepers (sleep duration ≥ 7 h, global sleep score ≤ 5, sleep latency ≤ 30 min and sleep efficiency >85%) had a higher energy distribution from dietary protein than poor sleepers. On the other hand, good sleepers showed a relatively lower percentage of energy from dietary carbohydrate and fat than poor sleepers. However, meta-regression analysis revealed no dose-dependent association between the macronutrient distributions and sleep duration. These results suggest that consuming a greater proportion of dietary protein may benefit on improving sleep quality in healthy adults. However, findings may be susceptible to reverse causality and additional RCTs are needed.

This study investigated the influence of selected maternal factors on the macronutrient composition and energy of human milk (HM). The study enrolled 159 breastfeeding mothers from five postpartum care centers in Seoul, Korea. Their gestational weeks were 37–42 weeks, they had no complications before and after childbirth, and were at 7–14 days postpartum. They provided data using structured questionnaires on general characteristics, stress, sleep quality, spousal support, and dietary intake. Breastfeeding assessment (LATCH) was investigated by qualified nurses, and each mother provided one sample of HM. The HM composition was analyzed using the Miris® HM analyzer. The relationships between variables were analyzed using Pearson’s correlation analysis, and a linear regression analysis was performed to verify the main variables. It was found that maternal dietary intake was related to HM composition as the %energy from carbohydrates (β = 0.86, p < 0.01) and %energy from fat (β = 0.77, p < 0.05) showed positive relationships with HM energy. The LATCH score was positively related to HM energy (β = 0.17, p < 0.05). In contrast, postpartum stress, sleep quality, and spousal support were not associated with HM macronutrient composition. In conclusion, HM macronutrients and energy content were associated with maternal dietary intake and LATCH scores, but not with postpartum stress, sleep quality, and spousal support.

Abstract Objectives Quality sleep is essential to health and poor sleep may increase the risk of obesity and type 2 diabetes. Previous studies have reported that macronutrient intakes, such as dietary protein, may be associated with sleep quality. However research, on this relationship in older adults who often exhibit poorer sleep quality is still limited. Therefore, the objective of this research is to assess the association between macronutrient intakes and sleep quality in middle-aged and older population in Singapore. Methods In this cross-sectional study, 104 men and women (59 ± 6 years) were recruited within Singapore. Their food intakes and sleep quality were assessed using 3-day food records and Pittsburg Sleep Quality Index questionnaire, respectively. In addition, plasma amino acid concentration was measured from the collected blood samples. The relationship between diet, plasma amino acids and sleep quality were evaluated using multiple linear regression and adjusted for age, BMI and gender. Results From this study, no association were observed between total energy intake (%E) from protein and tryptophan (Trp) intakes and sleep quality parameters. However, %E from fat was positively associated with sleep efficiency (SE%) (β-coefficient = 19.6, P = 0.02). A similar positive association with SE% was also observed for mono- and poly-unsaturated fatty acids. In contrast, %E from carbohydrate was inversely associated with SE% (β-coefficient = −21.2, P = 0.03). Sugar intake was also inversely associated with sleep quality parameters. Although plasma Trp concentration was not associated with sleep quality parameters, Trp and large neutral amino acids concentration ratio was inversely associated with sleep duration (β-coefficient = −19.2, P = 0.02). Conclusions In conclusion, consuming a diet with a higher energy from fat and lower energy from carbohydrate may improve sleep quality in Singapore middle-aged and older adults. The type of fat and carbohydrate consumed may also affect sleep quality. Funding Sources This research study is funded by the National University of Singapore (Grant number: R-143–000-A03–133).

The objective of this study was to examine the relationship between different sleep parameters and energy and macronutrient intake in school-aged children. A total of 203 children 6 to 9 years of age participated in this cross-sectional study. Anthropometric measurements were taken first. Diet was assessed with 3-day food logs and sleep was measured with a questionnaire on sleep quality and a wrist actigraph worn for at least 7 days. A decrease of 165.45 kcal was observed per each additional hour of sleep during the week (β (95% CI) = −165.45 (−274.01, −56.88); p = 0.003). This relationship was also observed for fat (β (95% CI) = −11.14 (−18.44, −3.84); p = 0.003) and protein (β (95% CI) = −13.27 (−22.52, −4.02); p = 0.005). An increase in weekend sleep efficiencies for those under the recommended threshold of 85% also had a similar association with energy (β (95% CI) = −847.43 (−1566.77, 128.09); p = 0.021) and carbohydrate (β (95% CI) = −83.96 (−161.76, −6.15); p = 0.035)) intake. An increase in habitual sleep variability was related with a slight increase in protein intake (β (95% CI) = 0.32 (0.031, 0.62); p = 0.031). Children who slept less had a higher energy intake, especially from fat and protein and those who presented inefficient sleep had a higher carbohydrate intake. Strategies to enhance sleep quality and quantity combined with dietary recommendations could help to improve energy and macronutrient intake levels in children.

Abstract Introduction We examined nationally-representative data on macronutrients associated with multiple dimensions of sleep health. Methods Data were obtained from the 2015–2016 National Health and Nutrition Examination Survey, (N=5,266 adults). Standard 24-h dietary recall procedures were analyzed to establish daily consumption of protein, carbohydrates, sugar, fiber, total fat, and saturated fat. Self-reported habitual sleep duration was categorized as very short (&lt;5h), short (5–6.5h), normal (7-8h), and long (&gt;8h). Sleep disturbance and daytime tiredness/fatigue were self-reported as either none, mild, moderate, or severe. Weighted multinomial logistic regressions with sleep variables as outcome/dependent variable and percent of each macronutrient as independent variable were adjusted for age, sex, race/ethnicity, education, and body mass index. Results Increased protein was associated with a decreased likelihood of very short sleep (RRR=0.01, p=0.019) and severe fatigue (RRR=0.06, p=0.020). Increased carbohydrates was associated with an increased likelihood of very short (RRR=61.17, p=0.001), short (RRR=3.96, p=0.017), and long (RRR=2.58, p=0.041) sleep, severe sleep disturbance (RRR=9.37, p=0.010) and fatigue (RRR=7.61, p=0.009). Increased sugar was associated with an increased likelihood of very short (RRR=24.17, p=0.001), short (RRR=3.29, p=0.017), and long (RRR=2.22, p=0.046) sleep, as well as mild (RRR=2.36, p=0.041) and severe (RRR=10.70, p=0.001) sleep disturbance, and severe fatigue (RRR=12.98, p&lt;0.0005). Increased fiber was associated with a decreased likelihood of long (RRR=0.01, p=0.032) sleep and severe sleep disturbance (RRR&lt;0.01, p&lt;0.0005), as well as moderate (RRR&lt;0.01, p=0.026) and severe (RRR&lt;0.01, p&lt;0.0005) fatigue. Increased fat was associated with a decreased likelihood of very short sleep (RRR=0.01, p=0.010). Increased saturated fat was associated with a decreased likelihood of very short sleep (RRR&lt;0.01, p=0.017). Conclusion Protein and fiber were associated with better sleep profiles overall and carbohydrate and sugar were associated with worse sleep, as well as increased prevalence of sleep disturbances and fatigue. Support Dr. Grandner is supported by R01MD011600

Abstract Objectives Several nights of moderate (4–5 hr/night) sleep restriction increases appetite and energy intake, and may alter circulating concentrations of food intake-regulating hormones. Whether more severe sleep restriction has similar effects is undetermined. This study aimed to determine the effects of severe, short-term sleep restriction on appetite and food intake-regulating hormones. Methods Randomized, crossover study in which 18 healthy men (mean ± SD: BMI 24.4 ± 2.3 kg/m2, 20 ± 2 yr) were assigned to three consecutive nights of sleep restriction (SR; 2 hr sleep/night) or adequate sleep (AS; 7–9 hr sleep/night) with controlled feeding and activity throughout the 3-day period. On day 4, participants consumed a standardized breakfast. Appetite ratings, and circulating ghrelin, peptide-YY (PYY), glucagon-like peptide (GLP-1), insulin, and glucose concentrations were measured before and for 4hr after the meal. Ad libitum energy and macronutrient intakes were then measured at a provided buffet lunch. Results Median PYY (–21%) and GLP-1 (–14%) concentrations were lower, and median glucose (3%) concentrations were higher after SR relative to after AS (main effect of condition, P &lt; 0.05; condition-by-time interaction, P = NS). Ghrelin and insulin did not differ between conditions. Mean hunger (–23%), desire to eat (–23%), and prospective consumption (–18%) ratings were all lower, and mean fullness (27%) ratings were higher after SR relative to after AS (main effect of condition, P &lt; 0.05; condition-by-time interaction, P = NS). Ad libitum energy intake at the lunch meal was 14% lower after SR relative to after AS (–332 kcal [95% CI: −479, −185] P &lt; 0.001), but macronutrient composition did not differ. Conclusions In contrast with reported effects of moderate sleep restriction, severe sleep restriction reduced appetite and energy intake. Appetite suppression was likely not due to the observed differences in food intake-regulating hormones as sleep-restriction induced reductions in PYY and GLP-1 concentrations would be expected to increase appetite. Funding Sources US Army MRDC. Authors’ views do not reflect official DoD or Army policy.

Background Obstructive Sleep Apnea (OSA) is considered a public health problem and its prevalence is increasing at an epidemic rate. The aim of this study was to examine whether individual nutrients (macronutrients, antioxidant vitamins) rather than energy restriction may potentially affect OSA severity in a representative population of Cyprus. Methods A total sample of 303 adults (>18 years old) with Cypriot citizenship and permanently residing in Cyprus were randomly selected. Selected patients have completed the food frequency questionnaire, and a physical activity questionnaire and underwent a sleep study to assess OSA severity. Results Overall, 303 patients were included in this study, 169 (55.8%) had mild OSA (apnea-hypopnea index—AHI <15) and the remaining 83 (27.4%) had moderate to severe OSA (AHI>15). The mean age of all patients was 55.7 years old. Patients with moderate to severe OSA had significant higher BMI levels, higher consumption of calories, higher hip circumference, waist circumference, waist-hip ratio and neck circumference and higher consumption of folic acid compared with the patients with mild OSA (p<0.05). Conclusions The findings suggest that increased energy intake regardless diet macronutrient composition is positively associated with OSA severity whereas higher folic acid intake seems to have a protective role.

Doctor of Philosophy(PhD)
Several studies have documented the direct effect of macronutrient intake on sleep. A general picture that has emerged indicates that a low carbohydrate diet with a total energy between 13-47% and high fat content with a total energy between 47-77% shows increases in slow wave sleep and may decrease rapid-eye movement sleep. However, previous studies investigating the association between carbohydrate meals and sleep have not explored the effects of the glycemic index (GI) of carbohydrate on sleep. This thesis investigated the affect of GI on the sleep pattern. In a cross-over, repeated measures design, we explored both the effect of GI and the timing of these meals on sleep in good sleepers. The effects of high and low GI carbohydrate-based meals given 4 h before the subjects’ usual bedtime on their sleep quality were examined in Chapter 3. Also evaluated was the effect of high GI meal timing (4 h vs. 1 h) on sleep. Twelve healthy men (18-35y, BMI 18.5-25 kgm-2) were administered a standard, isocaloric meal of low GI = 50 or high GI=109 in a cross-over and counter balanced manner, 4 h before their usual bedtime. On another occasion, the high GI meal was given 1 h before bedtime. Following the high or the low GI meal, participants underwent a familiarization sleep night followed by three polysomnographic test nights. The subjects’ blood and urine were collected for glucose and 6-sulfatoxymelatonin analysis respectively. Significant differences were found between the area under the curve (AUC) for blood glucose responses following the high GI meal compared to the responses for the low GI meal. It was shown that a carbohydrate-based high GI meal resulted in a significant shortening of sleep onset latency (SOL) in normal sleepers compared to a low GI meal (P = 0.009), and was most effective when consumed 4 h before bedtime (P = 0.01). There were no significant changes in other sleep indices. The Atkins’ Diet is a popular dietary therapy that promotes weight loss. This restricted carbohydrate diet with high fat and high protein content has not been evaluated for its effects on sleep, or systematically documented for its effects on mood, fatigue or sleepiness. The short term effect of the Atkins’ diet over 48 h on the sleep quality of healthy, non-obese males to a Control mixed diet was compared in Chapter 4. This study employed a repeated measure design where fourteen healthy, non-obese, good sleepers were given isocaloric diets and matching evening test meals (4 h before usual bedtime), which were either mixed (15% protein, 25% fat, 60% carbohydrate) or Atkins’ (38% protein, 61% fat, <1% carbohydrate). After a familiarization night with polysomnography, further polysomnographic testing was then performed on the Control night, 4 h after the first Atkins’ test meal (Atkins Acute) and 48 h (Atkins Ketosis) following commencement of the Atkins’ diet. Objective sleep was recorded using Compumedics S-series Sleep system; Compumedics Ltd, Melbourne, Australia. Urine ketone level was monitored before the evening test meals and at bedtime on the Control night, during the Atkins Acute and Ketosis phase. Blood glucose level was measured before the evening test meal until 120 min following the meal. Significant differences were found for the AUC for the blood glucose between the Control night and the Atkins Acute and Atkins Ketosis phase (P < 0.001). Participants developed mild hypoglycemia and ketosis 48 h following the Atkins’ diet. A significant reduction in the proportion of rapid eye movement (%REM) sleep to total sleep time (TST) was observed following the Atkins’ Acute and Atkins’ Ketosis phase compared to the Control (P = 0.006 and 0.05 respectively). The percentage of slow wave sleep (%SWS) to TST significantly increased for both the Atkins’ Acute and Ketosis phase compared to the Control meal (P = 0.02 for both phases). The sleep changes may be linked to the energy metabolism of fat of the Atkins’ diet. The effects of the Atkins’ diet compared to a Control mixed diet on sleepiness, mood, fatigue and dream recall were also investigated (Chapter 5). Participants’ overall daytime mood, fatigue intensity, sleepiness and other symptoms were assessed using a visual analogue scale before the evening test meals. The number of subjects with dream recalls was recorded on awakening after each polysomnographic night. The daytime symptoms of fatigue, sleepiness and depressed mood were significantly increased following the Atkins’ diet compared to the Control diet. A greater proportion of subjects reported dreams 48 h after the Atkins’ diet compared to either the Atkins’ Acute phase or the Control condition. Our findings suggest that mild hypoglycemia resulting from the diet may mediate the subjective responses of daytime sleepiness, depressed mood and intense fatigue. The increased proportion of subjects with dream recall may be related to an increased transient arousals from sleep during which dreams are usually consolidated into memory. The finding that “high-glycemic-index carbohydrate meals shorten sleep onset” may be relevant to persons with sleep disturbance. These meals may facilitate sleep transition for those with sleep initiation problems. The effect of the Atkins’ diet in SWS promotion and increasing feelings of fatigue and suppressing mood in the short-term may be relevant for patients with sleep apnoea (obesity), who experience low proportion of SWS and significant somnolence. Further studies to explore these effects on a longer term in this group would be worthwhile.

Miami-Dade County has approximately 27,000 people living with HIV (PLWH), and the highest HIV incidence in the nation. PLWH have reported several types of sleep disturbances. Caffeine is an anorexic and lipolytic stimulant that may adversely affect sleep patterns, dietary intakes and body composition. High caffeine consumption (>250 mg. per day or the equivalent of >4 cups of brewed coffee) may also affect general functionality, adherence to antiretroviral treatment (ART) and HIV care. This study assess the relationship of high caffeine intake with markers of disease progression, sleep quality, insomnia, anxiety, nutritional intakes and body composition. A convenience sample of 130 PLWH on stable ART were recruited from the Miami Adult Studies on HIV (MASH) cohort, and followed for three months. After consenting, questionnaires on Modified Caffeine Consumption (MCCQ), Pittsburg Insomnia Rating Scale (PIRS), Pittsburg Sleep Quality Index (PSQI), Generalized Anxiety Disorder-7 (GAD-7), socio-demographics, drug and medication use were completed. CD4 count, HIV viral load, anthropometries, and body composition measures were obtained. Mean age was 47.89±6.37 years, 60.8% were male and 75.4% were African-Americans. Mean caffeine intake at baseline was 337.63 ± 304.97 mg/day (Range: 0-1498 mg/day) and did not change significantly at 3 months. In linear regression, high caffeine consumption was associated with higher CD4 cell count (β=1.532, P=0.049), lower HIV viral load (β=-1.067, P=0.048), higher global PIRS (β=1.776, P=0.046), global PSQI (β=2.587, P=0.038), and GAD-7 scores (β=1.674, P=0.027), and with lower fat mass (β=-0.994, P=0.042), energy intakes (β=-1.643, P=0.042) and fat consumption (β=-1.902, P=0.044), adjusting for relevant socioeconomic and disease progression variables. Over three months, these associations remained significant. The association of high caffeine with lower BMI weakened when excluding users of other anorexic and stimulant drugs such as cocaine and methamphetamine, suggesting that caffeine in combination, but not alone, may worsen their action. In summary, high caffeine consumption was associated with better measures of disease progression; but was also detrimental on sleep quality, nutritional intakes, BMI and body composition and associated with insomnia and anxiety. Large scale studies for longer time are needed to elucidate the contribution of caffeine to the well-being of PLWH.

abstract: No studies have evaluated the impact of tracking resting energy expenditure (REE) and modifiable health behaviors on gestational weight gain (GWG). In this controlled trial, pregnant women aged >18 years (X=29.8±4.9 years) with a gestational age (GA) <17 weeks were randomized to Breezing™ (N=16) or control (N=12) for 13 weeks. The Breezing™ group used a real-time metabolism tracker to obtain REE. Anthropometrics, diet, and sleep data were collected every 2 weeks. Rate of GWG was calculated as weight gain divided by total duration. Early (GA weeks 14-21), late (GA weeks 21-28), and overall (GA week 14-28) changes in macronutrients, sleep, and GWG were calculated. Mediation models were constructed using SPSS PROCESS macro using a bootstrap estimation approach with 10,000 samples. The majority of women were non-Hispanic Caucasian (78.6%). A total of 35.7% (n=10), 35.7% (n=10), and 28.6% (n=8) were normal weight, overweight, and obese, respectively, with 83.3% (n=10) and 87.5% (n=14) of the Control and Breezing™ groups gaining above IOM GWG recommendations. At baseline, macronutrient consumption did not differ. Overall (Breezing™ vs. Control; M diff=-349.08±150.77, 95% CI: -660.26 to -37.90, p=0.029) and late (M diff=-379.90±143.89, 95% CI:-676.87 to -82.93, p=0.014) changes in energy consumption significantly differed between the groups. Overall (M diff=-22.45±11.03, 95% CI: -45.20 to 0.31, p=0.053), late (M diff=-23.16±11.23, 95% CI: -46.33 to 0.01, p=0.05), and early (M diff=20.3±10.19, 95% CI: -0.74 to 41.34, p=0.058) changes in protein differed by group. Nocturnal total sleep time differed by study group (Breezing vs. Control; M diff=-32.75, 95% CI: -68.34 to 2.84, p=0.069). There was a 11.5% increase in total REE throughout the study. Early changes in REE (72±211 kcals) were relatively small while late changes (128±294 kcals) nearly doubled. Interestingly, early changes in REE demonstrated a moderate, positive correlation with rates of GWG later in pregnancy (r=0.528, p=0.052), suggesting that REE assessment early in pregnancy may help predict changes in GWG. Changes in macronutrients did not mediate the relationship between the intervention and GWG, nor did sleep mediate relationships between dietary intake and GWG. Future research evaluating REE and dietary composition throughout pregnancy may provide insight for appropriate GWG recommendations.
Dissertation/Thesis
Doctoral Dissertation Nutrition 2019

The medical complications after bariatric surgery vary based on the procedure performed. Medical complications should be considered at specific phases after surgery. The various stages are: phase one (1 to 6 weeks), phase two (7 to 12 weeks), and phase three (13 weeks to 12 months). The various complications at each phase are discussed in this chapter, along with strategies to prevent postoperative complications. Finally, this chapter emphasizes the importance of the multidisciplinary postoperative evaluation of all bariatric surgery patients. The evaluation includes monitoring for health conditions as the patient loses weight, including hypertension, diabetes, sleep apnea, and hyperlipidemia. Monitoring the trajectory of weight loss, screening for micronutrient deficiencies, monitoring proper macronutrient intake, and assessment for development of late surgical complications are included in the multidisciplinary postoperative evaluation at all phases of follow-up.

Dating back to 400 bc, Hippocrates of Kos recognized the importance of nutrition and gut health, as well as that of sleep, in maintaining wellness. The gastrointestinal system (GI) is one of the largest organ system of the body and was once viewed as merely for alimentation and energy production. We now recognize the GI system’s influence on nearly every body system through the gut–brain connection and that it has bidirectional correlates with sleep health. Historical medicinal treatments that aided sleep and nervousness commonly were equally used for GI ailments. Within the GI system melatonin and neurotransmitters are synthesized and affect sleep. Circadian disturbances affect our microbiome and impact GI disorders such as gastroesophageal reflux, irritable bowel syndrome, and inflammatory bowel disease. Sleep restriction influences the types of foods eaten, and nutrition influences sleep health on both macronutrient and micronutrient levels. In 1989, J. Allen Hobson said “More has been learned about sleep in the last 60 years than in the preceding 6,000.” Today we may say the same thing about the microbiome, but what we know about the bidirectional nature of GI and sleep health is only the tip of the iceberg.