Browse the evidence

In individuals with type 2 diabetes who carry the haptoglobin (Hp) 2-2 phenotype, maintaining an HbA1c level below 6.5% does NOT significantly reduce the risk of coronary artery disease compared to levels of 7.0-7.9%.

If you have type 2 diabetes and have the haptoglobin 2-2 genetic variant, aiming for an HbA1c below 6.5% may not provide additional protection against heart disease compared to levels of 7.0-7.9%. This suggests that overly aggressive glycemic control might not be necessary or beneficial for cardiovascular health in this specific group.

Current precision nutrition products and nutrigenomics concepts promoted on social media frequently contain misleading or untrue information, particularly regarding the readiness of personalized diets and the ability of specific foods to alter DNA.

Be skeptical of social media posts claiming that specific diets or supplements can change your DNA or that personalized nutrition products are ready for general use. The scientific consensus is that these technologies are not yet ready for broad application, and many products contain misleading or untrue information. Focus on evidence-based dietary patterns rather than unproven genetic tailoring products.

In Type 2 Diabetes, Myocardial Infarction, Hypertension, and Sleep Apnea, elevated C-reactive protein (CRP) is primarily driven by adiposity rather than independent inflammatory pathology, as the association attenuates or reverses upon adjustment for BMI.

If you have high blood pressure, diabetes, or a history of heart issues, your high CRP (inflammation) is likely caused by your weight, not a separate inflammatory disease. Focus on weight loss through diet and lifestyle changes rather than seeking anti-inflammatory medications, as reducing adiposity directly reduces the inflammation.

In Chronic Kidney Disease, Heart Failure, Liver Disease, Psoriasis, Rheumatoid Arthritis, and COPD, elevated CRP is NOT explained by adiposity alone, indicating independent inflammatory pathology driven by end-organ damage or tissue-specific inflammation.

If you have CKD, Heart Failure, Liver Disease, Psoriasis, RA, or COPD, your high CRP is likely due to the disease process itself, not just your weight. While weight management is still beneficial, you likely need specific treatments targeting the organ damage or autoimmune response, as weight loss alone may not resolve the inflammation.

Switching from Tenofovir Alafenamide (TAF) to Tenofovir Disoproxil Fumarate (TDF) in women with HIV results in significant weight loss, indicating TDF has weight-suppressive effects.

If you are a woman with HIV and are gaining weight on a TAF-based regimen, ask your doctor about switching to TDF. Studies show this switch can lead to significant weight loss (approx. 1.6 kg) in women, likely because TDF suppresses weight gain compared to TAF.

Intensive lifestyle interventions (diet and exercise) aiming for weight loss and increased physical activity do not significantly reduce cardiovascular morbidity or mortality in patients with type 2 diabetes compared to standard care, despite improving surrogate markers.

While diet and exercise are crucial for feeling better and managing blood sugar, do not rely on them alone to prevent heart attacks or death in type 2 diabetes. You likely need medication to manage blood pressure and lipids alongside lifestyle changes to protect your heart.

In individuals with ideal cardiovascular health (high CVHS), elevated BMI (≥35.0 kg/m²) does not significantly increase the risk of incident atherosclerotic cardiovascular disease (ASCVD) compared to normal BMI, whereas low cardiovascular health strongly predicts ASCVD regardless of BMI.

If you have ideal cardiovascular health (normal blood pressure, lipids, glucose, no smoking, active, healthy diet, good sleep), your risk of heart disease is low even if your BMI is high (≥35). Focus on maintaining these healthy habits rather than trying to lose weight. If your cardiovascular health score is low, your risk is high regardless of your BMI, so prioritize improving lifestyle and metabolic markers.

High intake of food rich in saturated fat is not independently associated with subclinical atherosclerosis (measured by carotid intima-media thickness) or its progression in high-risk European adults after adjusting for lifestyle and demographic confounders.

For individuals at high risk of cardiovascular disease, simply reducing saturated fat intake may not be sufficient to reduce subclinical atherosclerosis markers like carotid intima-media thickness. Focus on overall lifestyle factors, including physical activity, smoking cessation, and managing other risk factors, as the isolated effect of saturated fat on this specific measure was not significant in this high-risk population.

Classifying individuals by a 'leptin phenotype' (relatively high or low leptin for a given body fat percentage) does not predict differential energy expenditure, metabolic adaptation, or susceptibility to weight change in response to caloric restriction or overfeeding.

Do not rely on fasting leptin levels to predict your weight loss success or failure. Whether your leptin is 'high' or 'low' for your body fat percentage does not appear to change how your body responds to dieting or overeating in terms of energy expenditure or weight change. Focus on sustainable caloric deficits and activity rather than hormonal profiling for prediction.

Markov models are the dominant methodological framework for economic evaluations of obesity interventions, with 85% of reviewed studies utilizing state-transition models.

When evaluating the long-term cost-effectiveness of obesity treatments, decision-makers should expect and look for Markov modeling. This is the standard approach for comparing interventions like bariatric surgery, pharmacotherapy, and lifestyle changes over a patient's lifetime.

Current Markov models for obesity interventions exhibit significant methodological heterogeneity, particularly in how they model the progression of obesity and its impact on costs and utilities.

When comparing economic evaluations of obesity treatments, be aware that different studies use different structures (direct BMI-to-cost vs. BMI-to-complications). This makes direct comparison difficult. Look for studies that use standardized validation and include a broad spectrum of complications.

Diabetes mellitus (DM) and cardiovascular disease (CVD) are the most frequently modeled obesity-related complications in Markov models, with DM appearing in 15/15 models focusing on complications and CVD in 14/15.

If you are looking at an economic evaluation of an obesity treatment, expect it to focus heavily on preventing diabetes and cardiovascular events. These are the main drivers of long-term cost savings and quality-of-life improvements in these models.

Validation practices in Markov models for obesity interventions are inconsistently reported, with only 43% of models reporting validation, limiting the reliability and comparability of these evaluations.

Be cautious when using economic evaluations of obesity treatments that do not report validation. Only 43% of the models reviewed reported validation, which limits their reliability. Look for studies that use standardized validation guidelines like ISPOR.

Self-reported weight and height data significantly overestimates obesity prevalence compared to professional anthropometric measurements, rendering self-perception an inadequate tool for population-level nutritional classification.

Do not rely on your own memory of your weight and height for health decisions. Self-reports are statistically unreliable for detecting obesity. If you are concerned about your nutritional status, get measured by a professional using calibrated scales and stadiometers, as self-reporting tends to overestimate obesity prevalence by missing the true extent of the issue.

Obtaining a home scale with advanced features (digital connectivity or high weight capacity >400 lbs) is significantly more expensive and less available than basic scales, creating a financial and accessibility barrier for patients requiring these specific features for self-weighing.

If you need to self-weigh but have a higher body weight or need to sync data with your doctor, basic scales may not work for you. These specialized scales cost significantly more ($40-$50+ vs $20-25). Talk to your clinician about whether insurance might cover the cost, or look for the most affordable option that meets your specific weight and connectivity needs.

Postprandial hypotension (PPH), defined as a systolic BP drop of ≥20 mmHg within 2 hours of eating, is an independent predictor of all-cause mortality, syncope, angina, and stroke, particularly in the elderly.

If you experience dizziness, fainting, or significant fatigue after meals, especially if you are older or have diabetes, consult a doctor. This could be postprandial hypotension, a risk factor for stroke and falls.

A 2-week exposure to either a low-carbohydrate (LC) or low-fat (LF) diet does not significantly alter sweet or salty taste detection thresholds or preferences compared to each other.

Switching to a low-carb or low-fat diet for just two weeks will not change your taste buds or your cravings for sweet and salty foods. If you are expecting your food preferences to shift quickly as a result of the diet itself, this short timeframe is likely too short to observe such changes.

Consuming ultra-processed milkshakes high in fat and sugar does not produce a statistically significant average increase in postingestive striatal dopamine release in healthy adults, refuting the hypothesis that such foods elicit drug-like dopamine responses.

The idea that ultra-processed foods trigger a massive, drug-like dopamine spike in your brain is not supported by this research. While individual responses vary, the average brain does not show a significant dopamine release after eating these foods. This suggests that overconsumption is likely driven more by behavioral factors, hunger, and hedonic preference (how much you like/want the food) rather than a fixed, overpowering biological addiction mechanism.

Adiposity (BMI, body fat percentage) is not significantly correlated with the magnitude of postingestive striatal dopamine response to ultra-processed foods, contradicting the hypothesis that obesity blunts this response.

Your body fat percentage does not determine how much your brain releases dopamine when you eat. This study found no link between being overweight and a 'blunted' dopamine response. This is encouraging: it means your brain's reward system is not permanently damaged by your weight, and you can influence your food preferences and intake through behavioral strategies.

Higher baseline BMI (30–40 kg/m²) correlates with lower health-related quality of life (utility indices), but the effect size is small (up to 0.005 per unit BMI) and often not statistically significant, suggesting BMI alone does not explain HRQoL differences.

While higher BMI is associated with slightly lower health utility scores, the impact is small and often not statistically significant. Do not assume your quality of life is defined by your BMI alone. Focus on managing specific comorbidities, age-related changes, and mental health, as these appear to be stronger drivers of your daily well-being than BMI itself.

Existing patient-reported outcome (PRO) measures for obesity fail to capture key appetite and eating behavior concepts (e.g., duration of fullness, satisfaction after eating, eating between meals) that are relevant to individuals with obesity and likely to change with treatment.

If you are managing obesity, standard questionnaires might miss how you actually feel after eating (satisfaction, fullness duration). This gap means your provider might not fully understand your specific challenges with cravings or portion control. A comprehensive assessment tool (like the EBAQ) can better capture these nuances to tailor your treatment plan.

Primary care physicians systematically overestimate the mortality risk associated with being overweight (BMI 25–29.9), perceiving a significantly higher risk increase than what current meta-analytic evidence supports.

Physicians should be aware that their perception of mortality risk for overweight patients (BMI 25-29.9) is likely inflated compared to actual population data. This overestimation may lead to unnecessary alarm or aggressive interventions for patients who are not obese. Counseling should focus on overall health metrics rather than BMI categories alone, recognizing that the mortality risk for 'overweight' is modest and inconsistent across studies.

DPP-4 inhibitors (Sitagliptin, Saxagliptin, Alogliptin, Linagliptin) are generally neutral for cardiovascular outcomes, with specific agents (Saxagliptin, Alogliptin) associated with an increased risk of heart failure hospitalization.

If you have Type 2 Diabetes and Heart Failure, avoid Saxagliptin and Alogliptin. DPP-4 inhibitors are generally neutral for heart health; prefer SGLT2 inhibitors or GLP-1 agonists which offer proven cardiovascular benefits.

In Japanese subjects, obesity (higher BMI) reduces the numerical incretin effect more significantly than glucose intolerance (T2D or IGT status).

If you have Type 2 Diabetes or pre-diabetes, focus on weight management to improve your body's natural hormonal response to food. In Japanese populations, losing weight improves the 'incretin effect' (how your gut signals your pancreas to make insulin) more effectively than just managing blood sugar numbers alone.