AIMS:To investigate whether the association between the nitrite-generating capacity of the subgingival microbiome and early cardiometabolic risk biomarkers varies by dietary nitrate intake. MATERIALS AND METHODS:Cross-sectional data from 668 participants (mean age 31 ± 9 years, 73% women) were analysed. Dietary nitrate intake was calculated from food frequency questionnaires. Subgingival 16S rRNA sequencing (Illumina, MiSeq) and PICRUSt2 estimated microbial genes. The Microbiome-Induced Nitric Oxide Enrichment Score (MINES) was calculated as a ratio of microbial gene abundances representing enhanced net capacity for NO generation. Adjusted multivariable linear models regressed cardiometabolic risk biomarkers (HbA1c, glucose, insulin, insulin resistance (HOMA-IR), blood pressure) on nitrate intake and MINES together with a MINES × nitrate intake interaction term. RESULTS:Mean nitrate intake was 190 ± 171 mg/day. Significant interactions of MINES and nitrate intake were observed for insulin and HOMA-IR (p < 0.05). Among participants with a low MINES, higher nitrate intake was associated with lower HOMA-IR (1.2 [1.1-1.4] vs. 1.5 [1.3-1.6]; p = 0.002), but levels were similar in those with high MINES (p = 0.84). CONCLUSIONS:A biomarker of higher microbial NO-generating capacity in subgingival plaque is associated with lower insulin and insulin resistance among individuals with lower dietary nitrate intake. Future trials evaluating the cardiometabolic benefits of nitrate-rich diets should incorporate measures of the entire oral microbiome.

AIM:We investigated whether periodontal measures are cross-sectionally associated with prediabetes and cardiometabolic biomarkers among non-diabetic younger adults. MATERIALS AND METHODS:One thousand seventy-one participants (mean age = 32.2 years [SE = 0.3]; 73% female) from the Oral Infections, Glucose Intolerance and Insulin Resistance Study were enrolled. Full-mouth clinical attachment loss (fm-CAL), probing depth (fm-PD) and bleeding on probing were ascertained. Interproximal CAL (i-CAL) and probing depths (i-PD) served as our primary exposures. Glucose, HbA1c, insulin and insulin resistance (HOMA-IR) outcomes were assessed from fasting blood. Prediabetes was defined per American Diabetes Association guidelines. Prediabetes prevalence ratios (PR [95% CI]) and mean [SE] cardiometabolic biomarkers were regressed on periodontal variables via multivariable robust variance Poisson regression or multivariable linear regression. RESULTS:Prevalence of prediabetes was 12.5%. Fully adjusted prediabetes PR in Tertiles 3 versus 1 of mean i-CAL was 2.42 (1.77, 3.08). Fully adjusted fasting glucose estimates across i-CAL tertiles were 83.29 [0.43], 84.31 [0.37], 86.48 [0.46]; p for trend <.01. Greater percent of sites with i-PD ≥3 mm showed elevated natural-log-HOMA-IR after adjustment (0%-12% of sites = 0.33 [0.03], 13%-26% of sites = 0.39 [0.03], ≥27% of sites = 0.42 [0.03]; p for trend = .04). CONCLUSIONS:i-CAL (vs. fm-CAL) was associated with elevated fasting glucose and prediabetes, whereas i-PD (vs. fm-PD) was associated with insulin resistance. Future studies are needed to examine periodontal disease and incident prediabetes.

At usual weight, energy intake and expenditure are coupled and covary to maintain body weight (energy stores). A change in energy balance, especially weight loss, invokes discoordinated effects on energy intake and output that favor return to previous weight. These regulatory systems reflect physiological changes in systems regulating energy intake and expenditure rather than a lack of resolve. The biological and behavioral physiology of dynamic weight change are distinct from those of attempts at static weight maintenance of an altered body weight. This suggests that optimal therapeutic approaches to losing or gaining vs. sustaining weight changes are different for most individuals.

ObesityVolume 29, Issue 8 p. 1238-1238 LETTER TO THE EDITOR Word selection and weight bias Louis J. Aronne, Louis J. Aronne Weill Cornell Medicine Comprehensive Weight Control Center, New York, New York, USASearch for more papers by this authorKevin D. Hall, Kevin D. Hall Laboratory of Biological Modeling, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USASearch for more papers by this authorJohn M. Jakicic, John M. Jakicic Healthy Lifestyle Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this authorRudolph L. Leibel, Rudolph L. Leibel Departments of Pediatrics and Medicine, Division of Molecular Genetics, Columbia University, New York, New York, USASearch for more papers by this authorMichael R. Lowe, Michael R. Lowe Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USASearch for more papers by this authorMichael Rosenbaum, Michael Rosenbaum Departments of Pediatrics and Medicine, Division of Molecular Genetics, Columbia University, New York, New York, USASearch for more papers by this authorSamuel Klein, Corresponding Author Samuel Klein sklein@wustl.edu orcid.org/0000-0001-7127-1156 Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA Correspondence Samuel Klein, Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA. Email: sklein@wustl.eduSearch for more papers by this author Louis J. Aronne, Louis J. Aronne Weill Cornell Medicine Comprehensive Weight Control Center, New York, New York, USASearch for more papers by this authorKevin D. Hall, Kevin D. Hall Laboratory of Biological Modeling, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USASearch for more papers by this authorJohn M. Jakicic, John M. Jakicic Healthy Lifestyle Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this authorRudolph L. Leibel, Rudolph L. Leibel Departments of Pediatrics and Medicine, Division of Molecular Genetics, Columbia University, New York, New York, USASearch for more papers by this authorMichael R. Lowe, Michael R. Lowe Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USASearch for more papers by this authorMichael Rosenbaum, Michael Rosenbaum Departments of Pediatrics and Medicine, Division of Molecular Genetics, Columbia University, New York, New York, USASearch for more papers by this authorSamuel Klein, Corresponding Author Samuel Klein sklein@wustl.edu orcid.org/0000-0001-7127-1156 Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA Correspondence Samuel Klein, Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA. Email: sklein@wustl.eduSearch for more papers by this author First published: 14 June 2021 https://doi.org/10.1002/oby.23203Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume29, Issue8August 2021Pages 1238-1238 RelatedInformation

Microbial communities along mucosal surfaces throughout the digestive tract are hypothesized as risk factors for impaired glucose regulation and the development of clinical cardiometabolic disease. We investigated whether baseline measures of subgingival microbiota predicted fasting plasma glucose (FPG) longitudinally. The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) enrolled 230 diabetes-free adults (77% female) aged 20 to 55 y (mean ± SD, 34 ± 10 y) from whom baseline subgingival plaque and longitudinal FPG were measured. DNA was extracted from subgingival plaque, and V3 to V4 regions of the 16S rRNA gene were sequenced. FPG was measured at baseline and again at 2 y; glucose change was defined as follow-up minus baseline. Multivariable linear models regressed 2-y glucose change onto baseline measures of community diversity and abundances of 369 individual taxa. A microbial dysbiosis index (MDI) summarizing top individual taxa associated with glucose change was calculated and used in regression models. Models were adjusted for age, sex, race/ethnicity, education, smoking status, body mass index, and baseline glucose levels. Statistical significance was based on the false discovery rate (FDR; <0.05) or a Bonferroni-corrected P value of 1 × 10-4, derived from the initial 369 hypothesis tests for specific taxa. Mean 2-y FPG change was 1.5 ± 8 mg/dL. Baseline levels of 9 taxa predicted FPG change (all FDR <0.05), among which Stomatobaculum sp oral taxon 097 and Atopobium spp predicted greater FPG change, while Leptotrichia sp oral taxon 498 predicted lesser FPG change (all 3 P values, Bonferroni significant). The MDI explained 6% of variation in longitudinal glucose change (P < 0.001), and baseline glucose levels explained 10% of variation (P < 0.0001). FPG change values ± SE in the third versus first tertile of the MDI were 4.5 ± 0.9 versus 1.6 ± 0.9 (P < 1 × 10-4). Subgingival microbiota predict 2-y glucose change among diabetes-free men and women.

In the 1940s, the diet-heart hypothesis proposed that high dietary saturated fat and cholesterol intake promoted coronary heart disease in "at-risk" individuals. This hypothesis prompted federal recommendations for a low-fat diet for "high risk" patients and as a preventive health measure for everyone except infants. The low carbohydrate diet, first used to treat type 1 diabetes, became a popular obesity therapy with the Atkins diet in the 1970s. Its predicted effectiveness was based largely on the hypothesis that insulin is the causa prima of weight gain and regain via hyperphagia and hypometabolism during and after weight reduction, and therefore reduced carbohydrate intake would promote and sustain weight loss. Based on literature reviews, there are insufficient randomized controlled inpatient studies examining the physiological significance of the mechanisms proposed to support one over the other. Outpatient studies can be confounded by poor diet compliance such that the quality and quantity of the energy intake cannot be ascertained. Many studies also fail to separate macronutrient quantity from quality. Overall, there is no conclusive evidence that the degree of weight loss or the duration of reduced weight maintenance are significantly affected by dietary macronutrient quantity beyond effects attributable to caloric intake. Further work is needed.

OBJECTIVE:The objective of this study is to determine whether resistance training is similarly effective in reducing skeletal muscle efficiency and increasing strength in weight-reduced and maximal weight subjects. METHODS:This study examined the effects of supervised resistance exercise on skeletal muscle in 14 individuals with overweight and obesity sustaining a 10% or greater weight loss for over 6 months and a phenotypically similar group of 15 subjects who had not reduced weight and were weight stable at their maximal lifetime body weight. We assessed skeletal muscle work efficiency and fuel utilization (bicycle ergometry), strength (dynamometry), body composition (dual energy x-ray absorptiometry), and resting energy expenditure (indirect calorimetry) before and after 12 weeks of thrice-weekly resistance training. RESULTS:Non-weight-reduced subjects were significantly (10%-20%) stronger before and after the intervention than reduced-weight subjects and gained significantly more fat-free mass with a greater decline in percentage of body fat than weight-reduced subjects. Resistance training resulted in similar significant decreases (~10%) in skeletal muscle work efficiency at low-level exercise and ~10% to 20% increases in leg strength in both weight-reduced and non-weight-reduced subjects. CONCLUSIONS:Resistance training similarly increases muscle strength and decreases efficiency regardless of weight loss history. Increased resistance training could be an effective adjunct to reduced-weight maintenance therapy.

Calories from any food have the potential to increase risk for obesity and cardiometabolic disease because all calories can directly contribute to positive energy balance and fat gain. However, various dietary components or patterns may promote obesity and cardiometabolic disease by additional mechanisms that are not mediated solely by caloric content. Researchers explored this topic at the 2017 CrossFit Foundation Academic Conference 'Diet and Cardiometabolic Health - Beyond Calories', and this paper summarizes the presentations and follow-up discussions. Regarding the health effects of dietary fat, sugar and non-nutritive sweeteners, it is concluded that food-specific saturated fatty acids and sugar-sweetened beverages promote cardiometabolic diseases by mechanisms that are additional to their contribution of calories to positive energy balance and that aspartame does not promote weight gain. The challenges involved in conducting and interpreting clinical nutritional research, which preclude more extensive conclusions, are detailed. Emerging research is presented exploring the possibility that responses to certain dietary components/patterns are influenced by the metabolic status, developmental period or genotype of the individual; by the responsiveness of brain regions associated with reward to food cues; or by the microbiome. More research regarding these potential 'beyond calories' mechanisms may lead to new strategies for attenuating the obesity crisis.

Periodontitis and type 2 diabetes mellitus are known to be associated. The relationship between periodontal microbiota and early diabetes risk has not been studied. We investigated the association between periodontal bacteria and prediabetes prevalence among diabetes-free adults. ORIGINS (the Oral Infections, Glucose Intolerance and Insulin Resistance Study) cross sectionally enrolled 300 diabetes-free adults aged 20 to 55 y (mean ± SD, 34 ± 10 y; 77% female). Prediabetes was defined as follows: 1) hemoglobin A1c values ranging from 5.7% to 6.4% or 2) fasting plasma glucose ranging from 100 to 125 mg/dL. In 1,188 subgingival plaque samples, 11 bacterial species were assessed at baseline, including Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Actinomyces naeslundii. Full-mouth clinical periodontal examinations were performed, and participants were defined as having no/mild periodontitis vs. moderate/severe periodontitis per the definition of the Centers for Disease Control and Prevention / American Academy of Periodontology. Modified Poisson regression evaluated prediabetes prevalence across bacterial tertiles. Prevalence ratios and 95% confidence intervals for third vs. first tertiles are presented. All analyses were adjusted for cardiometabolic risk factors. All results presented currently arise from the baseline cross section. Prediabetes prevalence was 18%, and 58% of participants had moderate/severe periodontitis. Prevalence ratios (95% confidence intervals) summarizing associations between bacterial levels and prediabetes were as follows: A. actinomycetemcomitans, 2.48 (1.34, 4.58), P = 0.004; P. gingivalis, 3.41 (1.78, 6.58), P = 0.0003; T. denticola, 1.99 (0.992, 4.00), P = 0.052; T. forsythia, 1.95 (1.0, 3.84), P = 0.05; A. naeslundii, 0.46 (0.25, 0.85), P = 0.01. The prevalence ratio for prediabetes among participants with moderate/severe vs. no/mild periodontitis was 1.47 (0.78, 2.74), P = 0.23. Higher colonization levels of specific periodontal microbiota are associated with higher prediabetes prevalence among diabetes-free adults.

Aim: Advanced glycation end products (AGEs) and/or their receptors (RAGE) are significantly positively correlated with adiposity, inflammation, dyslipidemia, and insulin resistance in adults. However, the relationships between AGEs, RAGE, and adiposity-related comorbidites in children have not been well studied. Methods: In a cross-sectional study of 88 children (age 11-15 years) from the New York area enrolled in the Reduce Obesity and Diabetes (ROAD) study, we examined the correlation of the AGE Nε-(carboxymethyl)lysine (CML), soluble RAGE (sRAGE), and endogenous secretory RAGE (esRAGE) with adiposity, inflammatory markers [interleukin-6 (IL-6), C-reactive protein, tumor necrosis factor-α], adiponectin, lipids, insulin sensitivity, and insulin secretory capacity. Results: Pediatric CML levels were ∼20% below average adult levels. CML was significantly (p < 0.05) positively correlated with age and insulin sensitivity and negatively with adiposity, dyslipidemia and IL-6. sRAGE correlated positively with esRAGE and negatively with adiposity and IL-6. Both sRAGE and esRAGE correlated negatively with insulin secretory capacity. Conclusion: Our findings suggest that unlike adults, CML is negatively associated with adiposity and adiposity-related comorbidity risk in children. As in adults, sRAGE and esRAGE were, to varying degrees, negatively correlated with body fatness and risk factors for adiposity-related comorbidities.

Sustaining weight loss by caloric restriction invokes metabolic (decreased energy expenditure), autonomic (decreased sympathetic and increased parasympathetic nervous system tone), neuroendocrine (decreased thyroid hormones and leptin), and behavioral (increased hunger) changes that conspire to favor the regain of lost weight [1]. The similarities between this “weight-reduced phenotype” and states of congenital leptin deficiency, and the remediation of these states in response to leptin repletion, suggest that the metabolic opposition to sustained weight loss is integrated in part via leptin signaling pathways [1]. A key question is whether the neural circuitry of the hypothalamus and other brain regions that mediate these responses can be influenced by specific aspects of diet. In this issue of Molecular Metabolism, McNay and Speakman [2] suggest that the macronutrient content of the diet affects both the anatomy and function of this circuitry.

RESEARCH DESIGN ANDMETHODSdThe continuous National Health and Nutrition Examination Survey 1999–2004 enrolled 3,616 participants (51% women) who received a periodontal examination and fasting blood draw. Participants weremean age (6 SD) 436 17 years and 28%Hispanic, 52%Caucasian, 17%AfricanAmerican, and 3%other. Log-transformed values of the homeostasis model assessment of insulin resistance (HOMA-IR) or HOMA-IR$3.30 (75th percentile) were regressed across full-mouth periodontal probing depth (PD) levels using linear and logistic models. White blood cell (WBC) count and C-reactive protein (CRP) were considered as either mediators or effect modifiers in separate analyses. Risk ratios (RRs) stem from marginal predictions derived from the logistic model. Results were adjusted for multiple periodontal disease and insulin resistance risk factors.