Tag: health

Weight Loss After 65: Treating Obesity in Older Adults

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

Obesity in older adults is a clinical problem about mobility, independence, and quality of life, and the number on the scale is the least interesting part of it. The problem is growing as the population ages. In the NHANES data, 38.9% of adults 60 and over meet criteria for obesity (Emmerich SD, NCHS 2024).

Between 2010 and 2050 the U.S. population over 65 will nearly double, from 40 million to more than 80 million (U.S. Census Bureau). Aging brings multimorbidity, frailty, and loss of independence with it, and excess adiposity adds a layer on top. Older adults with obesity are more likely to experience disability, reduced gait speed, and earlier institutionalization (Batsis JA, Eur J Intern Med 2014; Elkins JS, Obesity 2006). Body composition also shifts with age, with sarcopenia and visceral adiposity together making BMI a much less trustworthy measure than it looks (Batsis JA, Int J Obes 2016).

So the question is not really whether weight loss is safe in older adults. It is how it gets done. Intentional, structured weight loss helps when it is handled carefully. Randomized trials show that combining diet with exercise improves physical performance even when the weight loss itself is modest (Villareal DT, NEJM 2011; Batsis JA, J Am Geriatr Soc 2016). The danger sits with weight loss that is unintentional or too aggressive, which costs lean mass and bone density and raises fracture risk (Ensrud KE, J Clin Endocrinol Metab 2005). That is why the intervention has to protect muscle: resistance training, and protein intake of at least 1.0 to 1.2 g/kg/day, with leucine-rich sources where possible (Porter Starr K, J Gerontol Med Sci 2016).

Function is the goal, not the scale. Better mobility, more independence, better quality of life. Which means starting with an assessment of baseline function and cognition before recommending weight loss at all, and factoring in food security, transportation, and whether the patient is also caring for someone else. Physical therapists, dietitians, and care managers earn their place in this (Batsis JA, JAMDA 2011).

Medications are an option and they need care here. GLP-1 receptor agonists like semaglutide and liraglutide, and combinations like bupropion/naltrexone, are promising, but older adults have been poorly represented in the trials (Hollander P, Diabetes Care 2013; Grunvald E, Gastroenterology 2022). In practice hypotension, hypoglycemia, GI intolerance, and lean mass loss all carry more weight in a frail patient than they would in a younger one (Volpe S, Nutrients 2023; Batsis JA, Nat Rev Endocrinol 2018). I go slower on titration with these patients than the labeling suggests, and I ask about falls before I ask about weight.

What works is tailored and multidisciplinary. DASH or Mediterranean patterns are reasonable starting points. Activity should be gradual, supervised, and matched to ability, along the lines of the LIFE trial (Pahor M, JAMA 2015). Behavioral support delivered through community programs or telemedicine works in this population, which matters given how much harder it is for older patients to get to an office (Alberts SM, Gerontologist 2021; Batsis JA, BMC Geriatrics 2021). And sarcopenia, osteoporosis, and medication side effects all need watching throughout.

Treating obesity in older adults means preserving mobility, strength, and vitality rather than chasing an ideal weight. Done carefully, it buys people independence and dignity, which is what they came in for.

Scott Rennie, D.O.

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

How Does Aging Change Your Metabolism? What Research Shows

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

Obesity, diabetes, cardiovascular disease, and frailty get treated as though they were the price of getting older. Work from researchers like Luigi Fontana points somewhere else. Decades of metabolic strain do most of this damage, and diet, activity, and the choices stacked on top of them shape how much strain accumulates.

The scale is hard to ignore. Chronic disease accounts for close to 90% of U.S. healthcare spending, most of it aimed at managing complications rather than preventing them (Martin et al., Health Affairs, 2021). Life expectancy in this country fell by more than two years between 2019 and 2021. Obesity now affects 40.3% of adults aged 20 and older, with 9.7% in the severe range (1). Excess adiposity drives hypertension, diabetes, fatty liver disease, and several cancers.

The biology is familiar. Oxidative stress, inflammation, and insulin resistance sit underneath most of these conditions (Fontana & Partridge, Cell, 2015). Abdominal fat is active tissue rather than storage. It fuels insulin resistance and chronic inflammation, and it also activates bone marrow. In the PESA cohort of 745 apparently healthy adults imaged with FDG-PET, bone marrow activation tracked with every component of metabolic syndrome, and the activated group carried metabolic syndrome at 22.2% versus 6.7% (Devesa et al., Eur Heart J, 2022). Obesity changes the terrain of the body long before symptoms show up.

Calorie restriction without malnutrition is the clearest intervention we have. The animal data are strong. In rhesus monkeys, long-term restriction slowed age-related brain atrophy (Colman et al., Science, 2009), preserved muscle and function (Colman et al., J Gerontol, 2008), and lowered frailty and chronic disease burden (Yamada et al., J Gerontol, 2018).

This literature gets oversold, and the primate data are where it happens. The 2017 joint reanalysis of the two big studies found a survival benefit in the Wisconsin animals and no significant survival effect in the National Institute on Aging cohort (Mattison et al., Nat Commun, 2017). Health measures improved in both. Lifespan did not reliably follow. That is a more honest summary than the one usually quoted.

Human data are thinner but point the same direction. CALERIE tested roughly a 25% calorie reduction over two years and found improvements in blood pressure, cholesterol, glucose, inflammatory markers, heart rate variability, and insulin sensitivity (Meydani et al., Aging, 2016; Stein et al., Aging Cell, 2012; Weiss et al., Am J Clin Nutr, 2006). Even the more realistic reduction people actually achieved, closer to 12%, produced meaningful metabolic change (Kraus et al., Lancet Diabetes Endocrinol, 2019).

Calories are one variable. Nutrient quality and timing matter too. Studies in mice suggest high-protein diets may shorten lifespan, while restricting methionine or branched-chain amino acids improves metabolic health and longevity markers (Solon-Biet et al., Cell Metab, 2014). A trial in men with prostate cancer found that a single month of protein restriction lowered fat mass, cholesterol, and insulin (Fontana et al., Cell Rep, 2016). Protein source appears to matter as well, with plant-based sources holding an advantage over animal-based ones.

Timing has drawn its own attention. Intermittent fasting, whether through time-restricted eating or alternate-day fasting, extends lifespan in animal studies and protects against age-related disease including diabetes and cancer (Mattson et al., PNAS, 2014). Human studies suggest benefits on body fat, insulin sensitivity, and metabolic markers (Tosti et al., Aging Biology, 2022).

The gut microbiome adds a layer underneath all of it. Diet shapes microbial diversity and function, which in turn shapes inflammation, immunity, and metabolism (Thorburn et al., Immunity, 2014; Griffin et al., Cell Host Microbe, 2017). Fiber, protein type, and eating pattern all shift that balance. Nutrition never acts alone. It works through microbial partners.

What Fontana and others argue is that the real challenge lies less in treating diseases once they appear and more in holding metabolic integrity across a lifespan. That means healthcare built around prevention rather than reaction. Whole-food, plant-predominant eating. Fewer excess calories. Less reliance on protein-heavy patterns. Fasting strategies where they fit the patient. Attention to gut and immune health.

Aging isn’t a disease. Metabolic dysfunction is. Treat it early and seriously, through diet, lifestyle, and the interventions that have evidence behind them, and the curve bends toward years worth having.

Scott Rennie, D.O.

References:

1. National Center for Health Statistics. Prevalence of Overweight, Obesity, and Severe Obesity Among Adults Age 20 and Older: United States, 1960–1962 Through August 2021–August 2023. https://www.cdc.gov/nchs/data/hestat/obesity-adult-17-18/obesity-adult.htm

2. Martin AB, et al. Health Affairs. 2021.

3. Fontana L, Partridge L. Promoting health and longevity through diet: from model organisms to humans. Cell. 2015;161(1):106-118. https://pubmed.ncbi.nlm.nih.gov/25815989/

4. Fontana L, Kennedy BK, Longo VD, Seals D, Melov S. Medical research: treat ageing. Nature. 2014;511(7510):405-407. https://pubmed.ncbi.nlm.nih.gov/25056047/

5. Devesa A, et al. Bone marrow activation in response to metabolic syndrome and early atherosclerosis. Eur Heart J. 2022;43(19):1809-1828. https://pubmed.ncbi.nlm.nih.gov/35567559/

6. Colman RJ, et al. Science. 2009;325(5937):201-204.

7. Colman RJ, et al. J Gerontol A Biol Sci Med Sci. 2008;63(6):556-559.

8. Yamada Y, et al. J Gerontol A Biol Sci Med Sci. 2018;73(3):273-278.

9. Mattison JA, et al. Caloric restriction improves health and survival of rhesus monkeys. Nat Commun. 2017;8:14063. https://pubmed.ncbi.nlm.nih.gov/28094793/

10. Weiss EP, et al. Am J Clin Nutr. 2006;84(5):1033-1042.

11. Meydani SN, et al. Aging (Albany NY). 2016;8(7):1416-1431.

12. Stein PK, et al. Aging Cell. 2012;11(4):644-650.

13. Kraus WE, et al. Lancet Diabetes Endocrinol. 2019;7(9):673-683.

14. Solon-Biet SM, et al. Cell Metab. 2014;19(3):418-430.

15. Fontana L, et al. Cell Rep. 2016;16(2):520-530.

16. Mattson MP, et al. Proc Natl Acad Sci USA. 2014;111(47):16647-16653.

17. Tosti V, et al. Aging Biology. 2022.

18. Thorburn AN, et al. Immunity. 2014;40(6):833-842.

19. Griffin NW, et al. Cell Host Microbe. 2017;21(1):84-96.

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

Do Gut Bacteria Affect Your Weight? The Microbiome

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

When we talk about obesity, the conversation usually stops at calories and exercise. The trillions of microorganisms living in the gut deserve a place in it too. That ecosystem shapes metabolism, appetite, immune function, and the way the body handles stress. I have found that once patients understand the connection, they see weight and health in a very different light.

The microbiome covers more than bacteria. It includes archaea, fungi, viruses, and all their genetic material and metabolic products. The microbiota refers to the organisms themselves. None of it is passive. These organisms interact with diet, hormones, and the immune system in ways that either support health or push toward disease.

Diet does most of the shaping. Patterns rich in fiber and plant foods foster diversity and encourage species like Bifidobacteria and Bacteroides. Diets heavy in fat and low in fiber do the reverse. In older adults, that second pattern has been linked to frailty and worse health outcomes (Claesson et al., Nature, 2012).

The clinical relevance shows up in the obesity data. People with obesity tend to carry less diverse microbiomes and a greater capacity to pull energy out of food. In the foundational experiments, gut bacteria from obese mice were transplanted into germ-free mice, and the recipients gained more fat on the same caloric intake (Turnbaugh et al., Nature, 2006). The earlier work that set this up was a profiling study rather than a transplant, showing that ob/ob mice carry a different microbial composition than lean littermates (Ley et al., PNAS, 2005). Worth keeping those two straight, since they get merged constantly.

The mechanism comes down to metabolites. Microbes in the colon ferment fiber into short-chain fatty acids such as acetate, propionate, and butyrate. These influence GLP-1 and PYY, shift fat metabolism, and trigger inflammatory pathways that feed insulin resistance (Islam et al., Nutrients, 2022; Kong et al., Front Neurosci, 2021). High-fat diets also weaken the gut barrier, letting lipopolysaccharides leak into circulation. That process, metabolic endotoxemia, promotes systemic inflammation, insulin resistance, and weight gain (Kobyliak et al., Nutr J, 2016).

Antibiotic exposure adds another layer. A JAMA Network Open cohort followed 5,128 New Zealand children and found that 95% had received at least one antibiotic course before age four. Those with more than nine courses had 2.4 times the odds of obesity by age 4.5, and the association was strongest when exposure began before the first birthday (Chelimo et al., 2020). Animal models mirror it. Low-dose antibiotics given at weaning increased fat mass and altered metabolic pathways (Cho et al., Nature, 2012).

The microbiome talks to the brain as well. Microbial metabolites affect ghrelin, leptin, GLP-1, and CCK, the hormones that govern appetite, mood, and satiety (Van Son et al., Int J Mol Sci, 2021). That may be part of why chronic stress, anxiety, and disordered eating so often travel alongside changes in gut composition.

So where does this land in practice? Obesity treatment has to reach past calorie restriction. Supporting a healthy microbiome matters. Fiber-rich diets, probiotics, and prebiotics are under study as low-risk interventions. Synbiotics, which combine the two, are being evaluated as well. Fecal microbiota transplantation remains research territory. Even bariatric surgery outcomes may be partly explained by microbial shifts (Kovatcheva-Datchary et al., Cell Metab, 2015).

I have seen patients become more open to dietary change once they understand that what they eat feeds their microbes as much as it feeds them. That reframe carries weight. It moves the focus from restriction to partnership, from fighting the body to working with it.

Scott Rennie, D.O.

References:

1. Claesson MJ, et al. Gut microbiota composition correlates with diet and health in the elderly. Nature. 2012;488(7410):178-184. https://pubmed.ncbi.nlm.nih.gov/22797518/

2. Turnbaugh PJ, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444(7122):1027-1031. https://pubmed.ncbi.nlm.nih.gov/17183312/

3. Ley RE, et al. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA. 2005;102(31):11070-11075. https://pubmed.ncbi.nlm.nih.gov/16033867/

4. Islam MR, et al. Nutrients. 2022;14(3):624.

5. Kong D, et al. Front Neurosci. 2021;15:755845.

6. Kobyliak N, et al. Nutr J. 2016;15:43.

7. Chelimo C, et al. Associations of Prenatal and Childhood Antibiotic Exposure With Obesity at Age 4 Years. JAMA Netw Open. 2020;3(1):e1917577. https://pubmed.ncbi.nlm.nih.gov/31977058/

8. Cho I, et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature. 2012;488(7413):621-626. https://pubmed.ncbi.nlm.nih.gov/22914093/

9. Van Son J, et al. Int J Mol Sci. 2021;22(6):2993.

10. Kovatcheva-Datchary P, et al. Cell Metab. 2015;22(6):971-982.

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

Food Addiction and Obesity: How the Brain Is Involved

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

The human brain gets described as an engineering marvel. Like any product, it ships with vulnerabilities. Evolution built a system for surviving scarcity, and we now run that system in an environment of constant stimulation and engineered food. The mismatch explains a great deal about why obesity and addiction share so much ground.

One useful way to frame it is in terms of failure modes. Sometimes the design itself creates the problem. Sometimes development goes off track. And sometimes a perfectly good brain breaks down under conditions no brain was built for.

Take the design. We evolved to crave calorie-dense food because it was scarce and it kept us alive. Sugar and fat are now everywhere, and those old drives get hijacked. Food companies understand how to exploit them, the same way addictive substances exploit the same reward circuitry. The biology has not changed. The environment has.

Development matters too. Prenatal nutrition, early childhood adversity, and other disruptions shape how the brain handles reward and stress. Analysis of roughly 2,700 children in the NIH-funded ABCD Study found that higher BMI was associated with thinner cortex, particularly in prefrontal regions, and with lower working memory on list-sorting tasks (Laurent et al., 2020). Brain development itself appears alterable in the setting of poor diet and excess weight.

Then there are the extreme conditions. Trauma, chronic stress, and social adversity overwhelm coping systems, and food and drugs become the fallback. Calling that a failure of willpower misses what is happening. The brain is adapting, badly, to circumstances it can’t otherwise handle. It also helps explain why obesity and addiction cluster in groups facing economic hardship and unstable environments.

Dopamine sits at the center of both. Dopamine does more than produce pleasure. It teaches the brain what to attend to and what to repeat. Eat sugar, dopamine surges, the brain takes note. Use a drug, same signal. With repeated exposure, dopamine receptors downregulate (Volkow et al., 2013). Tolerance builds. Soon more sugar or more drug is needed to reach baseline.

Refined sugar is unusually effective in this loop. It spikes glucose fast, drives dopamine release, and slips past satiety signaling. Animal studies show sugar producing binge-like intake patterns and withdrawal signs on removal (Avena et al., Neurosci Biobehav Rev, 2008). In humans, high sugar intake has been linked to memory problems, greater inflammation, and impaired hippocampal function (Kendig, Appetite, 2014). Which is why cutting sugar feels less like breaking a habit and more like breaking an addiction.

So what helps? Supporting the brain at each stage. Protecting the developing brain through prenatal nutrition and limiting early sugar exposure. Teaching children coping skills, protecting sleep, and building activity, all of which strengthen the prefrontal cortex that reins in impulse. Reducing ultra-processed food at home and in schools.

Medications now target this signaling directly. GLP-1 receptor agonists act on satiety hormones in the gut and on brain pathways that regulate appetite. They reset the system rather than substituting for resolve.

Research is moving toward brain-based interventions: neurofeedback, brain stimulation, digital tools that reinforce healthier behavior in real time. The underlying message has not changed. Obesity and addiction are brain-based conditions shaped by biology, environment, and lived experience. Recognizing that changes how we treat and support the people in front of us, without letting anyone off the hook for their own care.

Scott Rennie, D.O.

References:

1. Laurent JS, et al. Associations Among Body Mass Index, Cortical Thickness, and Executive Function in Children. JAMA Pediatr. 2020;174(2):170-177. https://pubmed.ncbi.nlm.nih.gov/31816020/

2. Volkow ND, Wang GJ, Tomasi D, Baler RD. Obesity and addiction: neurobiological overlaps. Obes Rev. 2013;14(1):2-18. https://pubmed.ncbi.nlm.nih.gov/23016694/

3. Avena NM, Rada P, Hoebel BG. Evidence for sugar addiction: behavioral and neurochemical effects of intermittent, excessive sugar intake. Neurosci Biobehav Rev. 2008;32(1):20-39. https://pubmed.ncbi.nlm.nih.gov/17617461/

4. Kendig MD. Cognitive and behavioural effects of sugar consumption in rodents: a review. Appetite. 2014;80:41-54. https://pubmed.ncbi.nlm.nih.gov/24816323/

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

How Processed Food Disrupts the Gut Brain Connection

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

For years we told patients obesity came down to calories in and calories out. The research has moved. The gut-brain axis, the two-way traffic between the digestive system and the central nervous system, sits at the center of hunger, satiety, and reward. When it works, it regulates intake without conscious effort. Against the current food supply, it often doesn’t work.

The axis runs on neural, hormonal, and metabolic signals. After a meal, the gut reports what came in, how much, and when to stop. That system evolved for whole foods and scarce calories. It was never built for a diet dominated by processed, energy-dense products.

Alexandra DiFeliceantonio and Dana Small have argued that modern food disrupts the system by creating mismatches between what the gut senses and how the brain responds (Small & DiFeliceantonio, Science, 2019). Three features stand out.

The first is macronutrient combination. Fat and refined carbohydrate rarely appear together in high amounts in nature. Most processed food delivers both. People assign higher value to fat-carb combinations even when calories are held constant. In one controlled experiment, participants bid more money for foods containing both than for foods containing either alone (DiFeliceantonio et al., Cell Metab, 2018).

Speed of absorption is the second. Highly processed foods deliver calories fast, producing stronger responses in glucose metabolism and reward pathways (Carmody et al., PNAS, 2011; Hall et al., Cell Metab, 2019). Rodents develop stronger preferences for rapidly metabolized foods. In humans, faster eating rate tracks with weight gain.

Additives are the third. Sweeteners, emulsifiers, and artificial flavors make food more palatable while confusing the signaling underneath. When sweet taste stops reliably predicting calorie content, the brain loses the ability to regulate intake based on prior experience (Dalenberg et al., Cell Metab, 2020). The body learns that taste no longer matches nutrition.

The evidence supports the picture. People with obesity show altered valuation of high-reward foods compared with lean individuals (Perszyk et al., Nutrients, 2021). Rodents fed cafeteria-style diets full of energy-dense processed food overeat and gain weight, while chow-fed controls hold steady (Johnson & Kenny, Nat Neurosci, 2008; Beilharz et al., Front Psychol, 2014). Neuroimaging shows the striatum responding differently to fat-carb combinations than to single macronutrients (DiFeliceantonio et al., 2018).

All of which points at something uncomfortable. The modern food environment may overwhelm and distort the very systems built to keep intake in check. That is a real explanation for why “eat less, move more” fails so often. The advice assumes intact gut-brain signaling in people whose signaling is already compromised.

The open research question is how to restore it. Diets built around foods that reinforce reliable gut-brain communication. Studies of how additives affect hormonal and neural markers of satiety. Behavioral work helping patients relearn hunger and fullness cues.

Clinically, the thing worth holding onto is the environment patients are choosing inside. A food supply engineered to exploit biological vulnerabilities will beat most people’s intentions, and restoring the fidelity of that signaling may be where the real leverage sits.

Scott Rennie, D.O.

References:

1. Small DM, DiFeliceantonio AG. Processed foods and food reward. Science. 2019;363(6425):346-347. https://pubmed.ncbi.nlm.nih.gov/30679360/

2. DiFeliceantonio AG, et al. Supra-Additive Effects of Combining Fat and Carbohydrate on Food Reward. Cell Metab. 2018;28(1):33-44.e3. https://pubmed.ncbi.nlm.nih.gov/29909968/

3. Carmody RN, Weintraub GS, Wrangham RW. Energetic consequences of thermal and nonthermal food processing. Proc Natl Acad Sci USA. 2011;108(48):19199-19203. https://pubmed.ncbi.nlm.nih.gov/22065771/

4. Hall KD, et al. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain. Cell Metab. 2019;30(1):67-77.e3. https://pubmed.ncbi.nlm.nih.gov/31105044/

5. Dalenberg JR, et al. Short-Term Consumption of Sucralose with, but not without, Carbohydrate Impairs Neural and Metabolic Sensitivity to Sugar in Humans. Cell Metab. 2020;31(3):493-502.e7. https://pubmed.ncbi.nlm.nih.gov/32130879/

6. Perszyk EE, et al. Nutrients. 2021;13(11):3846.

7. Johnson PM, Kenny PJ. Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat Neurosci. 2010;13(5):635-641. https://pubmed.ncbi.nlm.nih.gov/20348917/

8. Beilharz JE, Maniam J, Morris MJ. Front Psychol. 2014;5:1454.

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

How Parents Influence a Child’s Weight and Eating

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

Childhood obesity has little to do with a child’s willpower. Biology, environment, and daily routine shape it. Genetics matter. So does the household, and that is where parents hold real leverage: how they feed, how they structure the day, what they model.

None of what follows is about blame. It is about where the leverage actually sits.

The clearest example starts in infancy. Responsive feeding means reading hunger and fullness cues instead of pressuring or ignoring them, and it has been linked to healthier eating patterns and weight gain matched to a child’s needs (Ventura, Adv Nutr, 2017). A parent who notices a baby turning away from the bottle and respects that signal is teaching self-regulation. The “clean your plate” approach many of us grew up with does the opposite. It overrides the signal and sets up overeating later (Johnson & Birch, Pediatrics, 1994).

Breastfeeding is where the popular version of this claim outruns the evidence. Observational studies associate exclusive and longer breastfeeding with lower obesity risk, with reductions sometimes quoted around 24%. The review most often cited for that number argues the observational literature is heavily confounded by socioeconomic status, maternal weight, and the feeding practices that travel alongside breastfeeding, and that randomized and sibling-comparison designs show a far weaker effect (Woo & Martin, Curr Obes Rep, 2015). Breastfeeding is worth supporting on its own merits. Promising parents it will prevent obesity goes past what the data support.

Parents also teach by example. A child who regularly sees a parent eating vegetables or trying something unfamiliar is more likely to do it. Repeated exposure paired with parental modeling makes children more willing to accept foods they would otherwise refuse. Using food as a reward runs the other way. Saying “you can have dessert if you eat your broccoli” teaches a child that sweets are the prize and broccoli is the toll (Newman & Taylor, J Exp Child Psychol, 1992).

The home environment does quiet work. Fruit and vegetables visible and easy to grab, energy-dense snacks harder to reach, and children drift toward the better option without a rule being enforced. Family meals matter too. The link to diet quality is consistent even where the direct effect on weight is murkier. They add structure and cut down on distracted eating.

Sleep and activity belong in the same conversation. Short sleep and heavy screen time in early childhood both raise obesity risk. Parents who hold bedtimes, encourage active play, and set limits on screens are shaping energy balance in ordinary daily ways.

Some strategies backfire. Restriction is the main one. In a well-known experiment, restricting children’s access to a particular snack increased both their desire for it and how much they ate when it became available, compared with an unrestricted food (Fisher & Birch, Appetite, 1999). Using food to soothe emotion has a similar problem. It builds an association between eating and comfort that persists into adult life.

Genetics play their part. Some children are more sensitive to food cues and less attuned to satiety, and twin studies put real numbers on that heritability (Wardle, Carnell & Plomin, Am J Clin Nutr, 2008). Even so, a supportive home makes a measurable difference in children carrying that predisposition. Responsive feeding, structure, and consistent modeling buffer inherited risk.

For families already struggling, family-based behavioral treatment has trial evidence behind it. The model runs on collaborative goal-setting, structured monitoring, and positive reinforcement, and it improves child weight outcomes in ways that hold up over time (Wilfley et al., JAMA Pediatr, 2017). Parent-only versions of the same treatment perform comparably to parent-and-child versions, which matters for families who can’t get everyone to an appointment (Boutelle et al., Appetite, 2021).

Parents don’t cause obesity. They do hold leverage points that matter, from infancy through adolescence, in how food, sleep, stress, and activity get managed at home.

Scott Rennie, D.O.

References:

1. Ventura AK. Does Breastfeeding Shape Food Preferences? Links to Obesity. Adv Nutr. 2017;8(1):149-150.

2. Johnson SL, Birch LL. Parents’ and children’s adiposity and eating style. Pediatrics. 1994;94(5):653-661. https://pubmed.ncbi.nlm.nih.gov/7936891/

3. Woo JG, Martin LJ. Does Breastfeeding Protect Against Childhood Obesity? Moving Beyond Observational Evidence. Curr Obes Rep. 2015;4(2):207-216. https://pubmed.ncbi.nlm.nih.gov/26100032/

4. Newman J, Taylor A. Effect of a means-end contingency on young children’s food preferences. J Exp Child Psychol. 1992;53(2):200-216. https://pubmed.ncbi.nlm.nih.gov/1578198/

5. Fisher JO, Birch LL. Restricting access to foods and children’s eating. Appetite. 1999;32(3):405-419. https://pubmed.ncbi.nlm.nih.gov/10336797/

6. Wardle J, Carnell S, Haworth CM, Plomin R. Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environment. Am J Clin Nutr. 2008;87(2):398-404. https://pubmed.ncbi.nlm.nih.gov/18258631/

7. Wilfley DE, et al. Dose, Content, and Mediators of Family-Based Treatment for Childhood Obesity. JAMA Pediatr. 2017;171(12):1151-1159. https://pubmed.ncbi.nlm.nih.gov/29084318/

8. Boutelle KN, et al. Appetite. 2021.

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

Childhood Food Insecurity: What Doctors and Families Can Do

On By Dr. Scott RennieIn Other Diseases and ConditionsLeave a comment

Food insecurity means the lack of consistent access to enough food for a healthy, active life. Hunger is part of it. The rest reaches into development, school performance, behavior, and long-term health, and millions of children in this country live inside it.

In pediatrics it surfaces quietly. A child who can’t concentrate at school. A family that skips meals at the end of the month. A growth curve that looks off and doesn’t fit the usual explanations. Treating it as a clinical problem rather than a social one is where the work starts.

The scope is wide. In 2024, 13.7% of U.S. households experienced food insecurity at some point during the year, and among households with children the figure was 18.4% (1). That is up sharply from the 10.5% recorded in 2020. Children carry the heaviest burden. The literature links food insecurity to iron-deficiency anemia, delayed motor and cognitive development, poor school performance, and behavioral problems including depression and inattention (Cook et al., J Nutr, 2004; Casey et al., Pediatrics, 2006).

Food insecurity and obesity travel together, which surprises most parents. Economic pressure pushes families toward calorie-dense, low-cost food. Scarcity itself can trigger binge eating when food becomes available. Stress and parenting under pressure add another layer. Children get urged to clean their plates, or food becomes the tool for soothing. Children aged 10 to 15 in food-insecure households are more likely to carry higher BMI and adiposity (Tester et al., Curr Obes Rep, 2020).

Geography tells its own story. The Southeastern U.S. carries some of the highest rates of both food insecurity and childhood obesity. Mississippi, Arkansas, Louisiana, New Mexico, and Texas rank among the hardest hit for food insecurity. Mississippi, West Virginia, Kentucky, Alabama, and Oklahoma consistently report the highest pediatric obesity rates. The overlap is no coincidence. Rural areas, tribal lands, and parts of Appalachia function as food deserts, where grocery stores are scarce and convenience stores and fast food fill the gap. Poverty and underinvestment in health infrastructure compound it.

So how do we find it? Most pediatric settings use the Hunger Vital Sign, a two-question screener drawn from the USDA’s 18-item scale and endorsed by the American Academy of Pediatrics. Against the full scale it runs 97% sensitive and 83% specific (Hager et al., Pediatrics, 2010; AAP Council on Community Pediatrics, Pediatrics, 2015). It asks families to respond to two statements:

“We worried whether our food would run out before we got money to buy more.”

“The food we bought just didn’t last, and we didn’t have money to get more.”

An answer of “often true” or “sometimes true” to either one signals risk. It is short, it embeds cleanly in an EMR, and it has been validated across languages. Longer tools exist, including the USDA’s full 18-item module, its 6-item short form, and the 9-item youth survey, but the time cost usually rules them out.

Screening is harder to implement than it sounds. Time, competing priorities, and plain discomfort discussing money are real barriers. Self-administered forms, EMR prompts, and universal framing all help. Telling every family “we ask everyone about food, because it’s central to health” takes the sting out of the question. When a screen comes back positive, the referral pathway has to already exist, whether that means SNAP and WIC enrollment, food pantries, or school meal programs.

Federal nutrition programs remain the strongest safety net. SNAP provides grocery support. WIC offers food vouchers, nutrition education, and breastfeeding support. The National School Lunch and School Breakfast Programs cover the school year, and the Summer Food Service Program covers the gap when school is out. The evidence doesn’t support the worry that these programs worsen obesity risk. WIC participation tracked with a decline in obesity among children aged 2 to 4 across 2010 to 2016 (Pan et al., MMWR, 2019). Stable SNAP benefits reduce the time children spend with obesity compared with non-participants (Au et al., J Nutr, 2019).

For clinicians the task splits in two: identify and connect. Screening is the first half. The impact comes from linking families to something real, which might mean a referral list built into the EMR, a relationship with a local enrollment center, or a standing partnership with a community food bank. Even asking “would it help if I connected you with resources that provide healthy food?” moves something.

Food insecurity is a health problem, and it shows up in front of us constantly. Naming it, screening for it, and acting on it protects children from consequences that reach a long way forward.

Scott Rennie, D.O.

References:

1. Rabbitt MP, et al. Household Food Security in the United States in 2024. USDA Economic Research Service, ERR-358, December 2025. https://www.ers.usda.gov/publications/pub-details?pubid=113622

2. Cook JT, et al. Food insecurity is associated with adverse health outcomes among human infants and toddlers. J Nutr. 2004;134(6):1432-1438. https://pubmed.ncbi.nlm.nih.gov/15173408/

3. Casey PH, et al. Child health-related quality of life and household food security. Pediatrics. 2006;118(5):e1406-e1413. https://pubmed.ncbi.nlm.nih.gov/17079542/

4. Hager ER, et al. Development and validity of a 2-item screen to identify families at risk for food insecurity. Pediatrics. 2010;126(1):e26-e32. https://pubmed.ncbi.nlm.nih.gov/20595453/

5. Council on Community Pediatrics, Committee on Nutrition. Promoting Food Security for All Children. Pediatrics. 2015;136(5):e1431-e1438. Reaffirmed 2021. https://publications.aap.org/pediatrics/article/136/5/e1431/33896/

6. Tester JM, Rosas LG, Leung CW. Food Insecurity and Pediatric Obesity. Curr Obes Rep. 2020;9(4):562-570.

7. Au LE, et al. J Nutr. 2019;149(9):1642-1650.

8. Pan L, et al. Trends in Obesity Among Participants Aged 2 to 4 Years in WIC, United States, 2010 to 2016. MMWR Morb Mortal Wkly Rep. 2019;68(45):1057-1061. https://www.cdc.gov/mmwr/volumes/68/wr/mm6845a2.htm

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

Childhood Obesity: How It’s Prevented and Treated

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

Childhood obesity is a chronic disease affecting roughly 14.7 million children and adolescents in the United States, and growth charts and BMI percentiles are the least interesting part of it. The American Academy of Pediatrics said as much in its 2023 Clinical Practice Guideline, which reframes obesity as a condition deserving the same urgency and structure we bring to any other chronic disease.

The guideline is built around 13 key action statements plus a set of consensus recommendations. The message running through all of them is that waiting doesn’t work. Early, structured intervention does.

The first shift is consistent screening. Pediatricians should measure height, weight, and BMI annually for every child between 2 and 18. Once BMI reaches the 85th percentile, the number stops being the point and the evaluation begins: dyslipidemia, prediabetes, fatty liver disease, hypertension, sleep apnea. That workup includes history, physical examination, and a careful look at social and environmental context.

For children over 10 with obesity, the guideline recommends a fuller lab evaluation. Fasting glucose or A1c, a lipid panel, liver enzymes, and where indicated a sleep study or a PCOS evaluation in adolescent girls. Depression screening belongs in that set too. Obesity travels with comorbidities, and treating one while ignoring the others misses most of the disease.

Once the diagnosis is made, treatment starts. Not next visit. The model is family-centered and non-stigmatizing, and motivational interviewing sits at the center of it because it lets clinicians surface ambivalence, name barriers, and set goals with families rather than at them.

The cornerstone is Intensive Health Behavior and Lifestyle Treatment. IHBLT is structured and sustained in a way brief counseling never is. The evidence supports at least 26 hours of face-to-face individual or group contact over 3 to 12 months, delivered by a multidisciplinary team of physicians, dietitians, behavioral health providers, and exercise professionals. That threshold is where outcomes start to move, and it applies to children as young as 6.

Nutrition counseling focuses on limiting calorie-dense, nutrient-poor food and increasing fruit, vegetables, and lean protein. Activity goals scale by age, with 60 minutes of moderate-to-vigorous movement daily as the benchmark for school-aged children. Behavioral strategies cover self-monitoring, goal setting, and problem solving. Parental involvement is central rather than optional, and programs that engage parents in the behavior change itself see better outcomes.

For families, this looks nothing like being told to eat better and move more. The intensity and the support structure are what shift the needle.

Not every family can reach a program like that. Geography, insurance, and local capacity all get in the way. The guideline acknowledges it and asks providers to deliver the most comprehensive care available while advocating for expanded community-based IHBLT.

Pharmacologic therapy is the next tier. Adolescents 12 and older may be offered FDA-approved weight-loss medication as an adjunct to health behavior and lifestyle treatment, according to each drug’s indications, risks, and benefits. Twelve is the floor. Medications are adjuncts to behavioral treatment rather than replacements for it.

Metabolic and bariatric surgery is addressed as well. For adolescents 13 and older with severe obesity, defined as BMI at or above 120% of the 95th percentile, the guideline supports referral to a comprehensive pediatric surgical center for evaluation.

The guideline also spends real attention on social determinants. Families dealing with poverty, food insecurity, systemic inequity, or nowhere safe to play are facing barriers that have nothing to do with individual willpower. Effective treatment has to acknowledge that and work on it where it can.

For practicing clinicians the roadmap is short: treat when obesity is identified, use motivational interviewing, refer to or provide IHBLT, manage comorbidities in parallel, and advocate for families against stigma and structural barriers.

Sandra Hassink, who helped lead the work, put the central point plainly when the guideline was released: “There is no evidence that ‘watchful waiting’ or delayed treatment is appropriate for children with obesity.”

Scott Rennie, D.O.

References:

1. Hampl SE, Hassink SG, Skinner AC, et al. Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents With Obesity. Pediatrics. 2023;151(2):e2022060640. https://publications.aap.org/pediatrics/article/151/2/e2022060640/190443/

2. Executive Summary: Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents With Obesity. Pediatrics. 2023;151(2):e2022060641. https://publications.aap.org/pediatrics/article/151/2/e2022060641/190440/

3. American Academy of Pediatrics. Clinical Practice Guideline for the Evaluation and Treatment of Pediatric Obesity: resources and implementation tools. https://www.aap.org/obesitycpg

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

What Brain Scans Show About Appetite and Overeating

On By Dr. Scott RennieIn Weight Loss & Metabolic Health1 Comment

Patients say a version of the same thing constantly: “I know what I should eat, but I still crave the wrong things.” That gap between knowledge and behavior is what pushed researchers toward the brain. Functional MRI has shown that appetite runs on circuits that defend fat mass and respond to food cues, and that willpower is a small part of the story.

For years we leaned on BMI as the working definition of obesity. A BMI over 30 got the label, and the number explained nothing about why weight gain happened or why some patients struggle far more than others. Schwartz and colleagues reframed it in 2017 as “a disorder of energy homeostasis, characterized by the defense of an elevated body fat mass” (Schwartz et al., Endocr Rev, 2017). That definition earns its keep. It says the body is working to hold fat stores high, and that when weight comes off, biology answers with stronger hunger signaling, slower metabolism, and shifted hormones.

The gut-fat-brain conversation sits at the center. Leptin, ghrelin, insulin, GLP-1, and PYY all shape hunger and satiety, and fMRI shows how those signals land. High-calorie food cues light up the amygdala, striatum, medial orbitofrontal cortex, and ventral tegmental area, all reward and craving territory (Schur et al., Int J Obes, 2009; Melhorn et al., Am J Clin Nutr, 2018). After weight loss, that reward response doesn’t fade, which is a large part of why relapse is the rule. Interventions do move it. Leptin replacement, intranasal insulin, GLP-1 agonists, and bariatric surgery all reduce this activation (Holsen et al., Int J Obes, 2018; van Bloemendaal et al., Diabetes, 2014).

One finding deserves more attention than it gets: looking at pictures of calorie-dense food predicts what people actually eat. In studies where participants later chose from a buffet, those with higher reward activation to food images selected more high-fat, high-calorie items. The brain response translated into behavior at the table.

That has treatment implications. Patients with persistent reward-driven responses may get the most from GLP-1 agonists like semaglutide. For others, agents acting on central insulin or leptin signaling may fit better. Bupropion-naltrexone targets reward pathways directly and may suit patients where hedonic eating is the main driver. Obesity is a brain-based condition, and it needs brain-aware treatment.

Inflammation belongs in this picture too. Valdearcos and colleagues showed that rodents on a high-fat diet developed hypothalamic gliosis, an inflammatory response in the brain, before they gained significant weight (Valdearcos et al., Cell Metab, 2017). Human MRI findings line up. Individuals with obesity are more likely to show signs of hypothalamic gliosis (Schur et al., Obesity, 2015; Kreutzer et al., Diabetes, 2017). Inflammation may disrupt appetite regulation early, helping drive the defense of elevated fat mass.

For clinicians, this changes the posture. Blaming patients for “failing” when weight returns misreads the physiology. Their biology is built to resist fat loss. Medications acting on appetite centers belong in long-term care rather than short courses. Diet quality may matter for brain inflammation as well as calorie balance. And as with any other chronic disease, the expectation should be continuous management rather than a one-time fix.

Framing obesity as a chronic brain and inflammatory disease does something useful for the room. It takes stigma out of it. Patients are living with a condition in which the brain defends fat mass through powerful signals, and that framing replaces shame with something we can actually treat.

Scott Rennie, D.O.

References:

1. Schwartz MW, Seeley RJ, Zeltser LM, et al. Obesity Pathogenesis: An Endocrine Society Scientific Statement. Endocr Rev. 2017;38(4):267-296. https://pubmed.ncbi.nlm.nih.gov/28898979/

2. Schur EA, et al. Activation in brain energy regulation and reward centers by food cues varies with choice of visual stimulus. Int J Obes (Lond). 2009;33(6):653-661. https://pubmed.ncbi.nlm.nih.gov/19365394/

3. Melhorn SJ, et al. Am J Clin Nutr. 2018;107(4):574-582.

4. Holsen LM, et al. Int J Obes (Lond). 2018;42(4):785-793.

5. van Bloemendaal L, et al. GLP-1 receptor activation modulates appetite- and reward-related brain areas in humans. Diabetes. 2014;63(12):4186-4196. https://pubmed.ncbi.nlm.nih.gov/25071023/

6. Valdearcos M, et al. Microglial Inflammatory Signaling Orchestrates the Hypothalamic Immune Response to Dietary Excess and Mediates Obesity Susceptibility. Cell Metab. 2017;26(1):185-197.e3. https://pubmed.ncbi.nlm.nih.gov/28683286/

7. Kreutzer C, et al. Hypothalamic Inflammation in Human Obesity Is Mediated by Environmental and Genetic Factors. Diabetes. 2017;66(9):2407-2415. https://pubmed.ncbi.nlm.nih.gov/28576837/

8. Schur EA, et al. Radiologic evidence that hypothalamic gliosis is associated with obesity and insulin resistance in humans. Obesity (Silver Spring). 2015;23(11):2142-2148. https://pubmed.ncbi.nlm.nih.gov/26530930/

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.

Doctor Supervised Weight Loss: What Works Long Term

On By Dr. Scott RennieIn Weight Loss & Metabolic HealthLeave a comment

Telling patients to eat less and move more doesn’t cut it. Obesity is a chronic disease, and progress requires structured, ongoing, individualized care. The hardest part clinically is making sure the weight that comes off is fat rather than muscle.

Losing muscle costs more than strength. It costs independence, recovery capacity, and eventually survival. Older adults and patients with low baseline activity are the most exposed. Poorly managed weight loss produces sarcopenia, the loss of muscle mass and function. Layer excess fat on top and you get sarcopenic obesity, where a patient looks heavy and is functionally weak and metabolically compromised at the same time.

The European Working Group on Sarcopenia in Older People sets out how to catch it early. It starts with loss of strength, measured by grip strength or a chair-stand test. DXA or BIA can confirm low muscle mass. Poor strength plus low mass plus reduced physical performance defines severe sarcopenia. These definitions give us a framework to act before decline becomes permanent (Cruz-Jentoft et al., Age Ageing, 2019).

Muscle mass predicts survival on its own. Appendicular Lean Mass Index, lean tissue in the arms and legs divided by height squared, is a reliable measure, and people in the top quartile carry substantially lower all-cause mortality than those in the lowest, even after adjusting for BMI and comorbidities (Srikanthan & Karlamangla, Am J Med, 2014). Put plainly, a patient with stronger arms and legs has better odds of living longer regardless of what the scale says.

Strength testing matters as much as mass. Low grip strength tracks with higher mortality from cardiovascular disease, cancer, and respiratory illness. Poor lower-body strength, showing up as slower gait speed or difficulty rising from a chair, tracks with frailty, hospitalization, and long-term disability.

Both measures are cheap and fast, and both are underused. Grip strength needs a dynamometer and a hand to put it in, which rules it out of any remote encounter. The 30-second sit-to-stand doesn’t. A hard chair, arms crossed over the chest, as many stands as the patient can complete in thirty seconds, counted by whoever is watching. It works over video as well as it works in a room, and it yields a number worth tracking across visits. One usable functional measure beats a chart full of weights.

Nutrition is the cornerstone of preserving muscle during weight loss. The baseline protein RDA of 0.8 g/kg/day is inadequate for many adults, particularly older patients and anyone in a calorie deficit. The evidence supports closer to 1.2 to 1.6 g/kg/day, and up to 2.0 g/kg/day in some medically supervised cases (Paddon-Jones et al., Am J Clin Nutr, 2015; Bauer et al., J Am Med Dir Assoc, 2013). Spacing intake across meals rather than loading it at dinner sustains muscle protein synthesis better. Whey and casein, soy, eggs, and blended plant sources all work.

Calculating a target in patients with obesity is genuinely awkward. Actual body weight overshoots. Ideal body weight undershoots. Adjusted body weight is the usual compromise, and reasonable clinicians disagree about which to use. For a 5’6″ patient, a target weight around 73 kg at 1.5 g/kg puts the daily goal near 110 grams, which is a number a patient can actually work with.

Exercise has to include both resistance and aerobic work. Resistance training protects and builds lean mass. Aerobic activity improves cardiovascular and metabolic health. A 2022 systematic review and meta-analysis found the combination conferred the greatest mortality risk reduction, and notably found that weight training alone wasn’t associated with lower mortality among people doing no aerobic exercise (Shailendra et al., Am J Prev Med, 2022). For a patient that translates to brisk walking or cycling most days plus two or three weekly strength sessions using bands, weights, or bodyweight.

Think of muscle like a retirement account. Build it and hold it early, so it is there when it is needed. A patient in their seventies who lands in the hospital with pneumonia can lose a large fraction of their reserve to a few days of immobility and poor intake. If the reserve was thin going in, the decline may be permanent, and it shows up later as falls, fractures, and lost independence. Investing ahead of time changes that trajectory.

Medication is a real adjunct, and it has to be paired with strategies that protect lean tissue. GLP-1 receptor agonists produce substantial weight loss, and a meaningful fraction of that loss is lean mass. Adequate protein and resistance training are the mitigation strategies with actual evidence behind them, and body composition deserves monitoring wherever it is available rather than weight alone.

As clinicians, we need to screen past BMI. Ask about physical activity. Get a functional measure. Consider dietary adequacy. Refer to dietitians, physical therapists, or trainers where it helps, and even a single session with a trainer improves safety and confidence with resistance work. Set goals patients can hold: one to two pounds a week of fat loss while protecting muscle.

Obesity treatment is chronic care. The point is helping patients hold strength, mobility, and independence while metabolic health improves, and preserving muscle sits at the center of that.

Scott Rennie, D.O.

References:

1. Cruz-Jentoft AJ, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31. https://pubmed.ncbi.nlm.nih.gov/30312372/

2. Srikanthan P, Karlamangla AS. Muscle mass index as a predictor of longevity in older adults. Am J Med. 2014;127(6):547-553. https://pubmed.ncbi.nlm.nih.gov/24561114/

3. Paddon-Jones D, et al. Protein and healthy aging. Am J Clin Nutr. 2015;101(6):1339S-1345S. https://pubmed.ncbi.nlm.nih.gov/25926511/

4. Bauer J, et al. Evidence-based recommendations for optimal dietary protein intake in older people: a position paper from the PROT-AGE Study Group. J Am Med Dir Assoc. 2013;14(8):542-559. https://pubmed.ncbi.nlm.nih.gov/23867520/

5. Shailendra P, et al. Resistance Training and Mortality Risk: A Systematic Review and Meta-Analysis. Am J Prev Med. 2022;63(2):277-285. https://pubmed.ncbi.nlm.nih.gov/35599175/

Board Certified in Obesity Medicine and Family Medicine

This blog is for educational purposes only and does not constitute individual medical advice. Always consult your own physician before making changes to your health, medications, or treatment plan.