She grabbed an almond milk from the hospital basement, thinking it was the healthy choice. Her glucose sensor told a different story.
Dr. Sue-Anne Toh is an endocrinologist, a clinical scientist, a former healthcare system executive at the National University Hospital, and the founder of NOVI Health, a technology-enabled metabolic health clinic in Singapore. She has spent over two decades studying how the body processes energy, stores fat, and slides toward disease. She also runs one of the most deeply phenotyped longitudinal diabetes prevention studies in Asia.
And the first time she wore a continuous glucose monitor herself, a drink she had always considered healthy spiked her blood sugar to 10.9 mmol/L.
I sat down with Dr. Toh in Singapore to talk about what this small, fast-developing nation has learned about chronic disease that the rest of the world has been slow to absorb. What she described reframed metabolic risk, body composition, and what personalized medicine actually looks like when it is done with both rigour and empathy.
Singapore’s National Security Threat: How One Nation Made Prevention Policy
Singapore went from developing nation to one of the wealthiest countries on earth in roughly fifty years. That speed produced a natural experiment in metabolic health that few other populations can match.
The chronic disease trends mirror the global pattern, but the rate of acceleration is staggering. Diabetes prevalence among Singaporean adults aged 18 to 74 rose from 8.8% in 2017 to 9.5% in 2020, and currently plateaued at 9.1% in 2024. International estimates project Singapore to rise to 13% by 20251. The difference is that in Singapore, the complications of metabolic disease appear to show up at lower thresholds than in Western populations. A body mass index that would be considered merely overweight by WHO standards is already producing the vascular damage, insulin resistance, and organ stress that a BMI of 30 produces elsewhere.
This is not a Singaporean anomaly. The standard WHO cutoffs classify overweight at a BMI of 25 and obesity at 30. Singapore, along with much of Asia, has adopted lower thresholds: 23 for overweight and 27.5 for obesity, because Asian populations develop type 2 diabetes and cardiovascular disease at BMIs well below the global cutoffs2. At a BMI of 25, Asians carry 3 to 5% more total body fat than white Europeans at the same BMI3. Using the WHO cutoff of 30, Singapore’s obesity prevalence sits at roughly 12.7%. Apply the Asian-specific threshold of 27.5, and prevalence nearly doubles, to approximately 22.8%, with significant variation across ethnic groups: 46.4% among Malay Singaporeans, 35.4% among Indian Singaporeans, and 16.8% among Chinese Singaporeans4.
“We can hide fat really well in our livers, in internal organs,” Dr. Toh told me. Asian populations are disproportionately affected by visceral adiposity, the accumulation of fat around and within organs rather than beneath the skin. A person can look lean and carry a metabolic burden that a standard physical examination would never detect. The clinical shorthand for this is TOFI: thin outside, fat inside. Research comparing Asian Chinese and European Caucasian adults matched for gender, age, and BMI has confirmed that Asian populations are more susceptible to visceral adipose tissue deposition and subsequent metabolic dysregulation5. This mismatch isn’t limited to East Asians. South Asians, including Singapore’s Indian community, often show an even more pronounced “thin-fat” phenotype, with higher visceral fat and diabetes risk at lower body weights6. It is one of the reasons that relying on BMI as a primary screening tool misses so many people who are already on the path toward disease.
Singapore did something unusual in response. In 2016, the government declared a “war on diabetes,” making it one of the first nations to treat metabolic disease as a national security-level threat rather than an individual lifestyle problem.
The declaration was not rhetorical. It produced policy changes in the built environment, food labelling, school nutrition, healthcare financing, and clinical screening protocols. The country invested in making the healthier choice the easier choice: an excellent public transport system that requires walking between connections, making car ownership deliberately expensive, and building physical activity into urban design rather than treating it as something citizens should find time for on their own.
The APT-2D Study: Why Insulin Secretion, Not Just Resistance, Is the Real Asian Story
Dr. Toh’s research programme launched at almost exactly the same time Singapore declared its war on diabetes, a coincidence of timing that she describes with a mixture of amusement and genuine scientific interest.
The APT-2D (Assessing Progression to Type-2 Diabetes) study is a deeply phenotyped observational cohort following individuals who are metabolically normal or have prediabetes, tracking their progression toward type 2 diabetes7. What makes it unusual is the granularity of the physiological testing. Rather than relying solely on fasting glucose and HbA1c, the study uses gold-standard methods, including hyperinsulinemic-euglycemic clamps and frequently sampled intravenous glucose tolerance tests, to assess insulin secretion and insulin resistance separately in thousands of participants.
The central finding is one that should be more widely known than it is. In Western populations, type 2 diabetes has traditionally been understood as primarily a disease of insulin resistance: the body produces insulin, but the cells stop responding to it effectively, typically in the context of excess adiposity. In the APT-2D cohort, defects in insulin secretion, the pancreas’s capacity to produce insulin in the first place, appeared to play a relatively greater role in Asian populations than previously appreciated8.
This distinction matters for prevention and treatment. If the primary driver is resistance, the intervention logic points toward weight loss, exercise, and medications that improve insulin sensitivity. If secretion is a larger part of the problem, the picture shifts. Pancreatic reserve matters more. The timeline for intervention may be shorter. And the populations at risk may not look the way clinical screening tools expect them to look.
“If you don’t get a good night’s sleep, you wear a glucose sensor, you might see that your glucose levels will trend higher,” Dr. Toh said. “Some of these observations really spurred me to realize that it’s not good enough understanding biology, and it’s not good enough being a doctor that just intervenes with medications. We really need to go much further upstream to tackle the root of these issues.”
An unexpected finding emerged from the study’s timing. Because it coincided with the national diabetes campaign, the incidence of progression to type 2 diabetes in the cohort was lower than the historical rates on which the study had been powered. Whether that was because of improved national awareness, or because participants who are being closely monitored and receiving regular feedback about their own metabolic data tend to make better choices, is an open question. Dr. Toh suspects both are true.
“It also informs how we really need more contextualized interventions that are more precise and personalized,” she said, “and they don’t just involve drugs. Nutritional strategies, physical activity. Just moving for a few minutes after meals could really make a huge difference.”
Human-Gated AI in Real Life
The gap between knowing what works in theory and implementing it for a specific person in the complexity of their actual life is where most chronic disease prevention falls apart. Novi Health was built to close that gap.
Dr. Toh’s path from bench scientist to clinician to health system executive to entrepreneur was not planned, but each stage addressed the same problem from a different vantage point.
“It’s always been a singular focus of seeing a problem and not being able to unsee it,” she told me. “And realizing that to solve this massive problem, you really need a much more whole-society, multifaceted approach.”
A patient journey at NOVI begins with a consultation and a baseline assessment that may include bloodwork, imaging, body composition analysis, and wearable data. But it also begins with a question that most clinical encounters skip entirely: What are your goals?
“No two individuals necessarily have the same goals,” Dr. Toh said. “Some people will say, I don’t even want to live to a hundred. Others say, I want to be able to do this or that. We start there and work backwards.”
The clinical data, the lifestyle assessment, the wearable inputs, and the patient’s stated goals are assembled through Novi’s technology platform. AI tools handle data integration, pattern visualization, and longitudinal trend mapping. Then the output goes back to human clinicians for interpretation, communication, and shared decision-making with the patient.
“It’s always human-gated,” Dr. Toh emphasized. “The AI does the data crunching and visualization of patterns. Then we put it back in the hands, eyes, brains, and heart of the provider.”
The intervention architecture draws on the six pillars of lifestyle medicine: nutrition, physical activity, sleep, stress management, substance use, and social connection. Where gaps exist that lifestyle alone cannot close, pharmaceutical interventions are added with an evidence-based, measured approach, regularly reassessed for benefit versus risk. Supplements are treated the same way: not as defaults, but as targeted interventions with measurable endpoints.
What distinguishes this model from a standard chronic disease clinic is the feedback loop. Wearable data, selected for relevance rather than volume, is piped into a patient dashboard. Coaches work with the clinical team to help patients build habits around the data they are seeing. The positive change becomes visible on the dashboard, which reinforces the behaviour, which produces more positive change.
“People don’t like being reminded of failure,” Dr. Toh said. “Having meaningful progress indicators is very motivating. Positive progress is a motivator.”
The Almond Milk Moment
I asked Dr. Toh whether she had worn a continuous glucose monitor herself and what she had learned.
The story she told is one that will be familiar to anyone who has read my own CGM experience. She was working a heavy clinic day at the National University Hospital, had no time for a proper lunch, and grabbed a bottled almond milk drink from a health food store in the hospital basement. She assumed it was a reasonable choice. An hour and a half later, feeling slightly off, she scanned her sensor.
10.9 mmol/L. That is a reading between 196 and 200 mg/dL, well into the range that would concern a clinician in a patient with known diabetes. Dr. Toh does not have diabetes.
“I continued to observe my own patterns,” she said. “I realize that while I’m not yet diabetic or prediabetic, I might actually be inching quite close if I don’t watch it.”
She puts a sensor on periodically, not continuously, to reinforce her own understanding and behaviours. She notices that when she wears one, she loses a kilogram or two, simply from paying closer attention. She sequences her food differently: protein and vegetables before carbohydrates. She notices differences in energy and cognition that correlate with glucose patterns.
“A lot of our patients say this has been wonderful, because all the things I thought I couldn’t eat, that I like to eat, turns out they don’t spike my glucose at all,” she said. “Whereas other things I was forcing myself to eat that I did not enjoy, actually do spike. It’s so liberating.”
That liberation is not universal. Dr. Toh is clear about the limitations.
“It’s not a one-size-fits-all,” she said. “There are people who would get unduly anxious, not be able to act well on that, and it could have unintended consequences that could be more harmful to them.”
This mirrors the Weizmann Institute findings and the PREDICT 1 study: glycemic responses to identical foods vary enormously between individuals, and population-level dietary advice based on glycemic index is a blunt instrument at best9. The CGM makes the personal pattern visible. What you do with that visibility depends on who you are and what support you have around you
.
The GLP-1 Revolution: Measured Enthusiasm from an Endocrinologist Who’s Seen it All
I could not sit with an endocrinologist in 2025 and not ask about GLP-1 receptor agonists.
Dr. Toh’s response was measured and genuinely enthusiastic in a way that surprised me, given her earlier skepticism about longevity medicine marketing.
“As an endocrinologist, having been in the field for over two decades, there has never been a more exciting time,” she said. The medications she is referring to, semaglutide (Ozempic, Wegovy) and tirzepatide (Mounjaro, Zepbound), work by mimicking hormones that regulate appetite, insulin secretion, and gastric emptying. They are producing weight loss and cardiovascular outcome improvements at a scale that no previous pharmaceutical intervention has achieved for metabolic disease10.
“They really are doing something fundamental to correct this imbalance,” Dr. Toh said. “They can empower significant numbers of people to make a change in trajectory for the better.”
She framed obesity not as a standalone condition but as a hub with spokes extending into mental health, mobility, chronic pain, sleep quality, and social participation. Correcting the metabolic imbalance at the centre can produce cascading improvements across multiple dimensions of wellbeing.
The caution she would add, and did, is that these medications are tools within a broader system, not replacements for the behavioural, nutritional, and structural changes that sustain health over decades. The most powerful intervention remains the one she offered as her first piece of advice.
Three Things She Would Tell Everyone
At the end of our conversation, I asked Dr. Toh for one recommendation. She asked if she could give three.
First, for individuals: move after your biggest meal. Even two minutes of walking or light activity after eating helps prevent glucose spikes regardless of where you are on the metabolic spectrum. This is one of the most consistently supported findings in the literature, confirmed by multiple meta-analyses, and it costs nothing11.
Second, for everyone: take sleep seriously. “Deeply busy populations take it for granted,” she said. If you are not getting a minimum of six hours, you are unlikely to accumulate enough deep and REM sleep to restore the systems that regulate glucose, appetite, and cognitive function. If you are getting the quantity but still not waking refreshed, talk to a provider who can investigate why. The causes range from sleep apnoea to nutritional deficiencies to blue light exposure, and many of them have relatively simple solutions.
Third, for policymakers: make real food more accessible. Food is medicine is not a metaphor. It is a policy position. And in most countries, the food environment is still engineered to make the least healthy options the most convenient and affordable.
Three Non-Negotiables: Move After Meals, Protect Sleep, Demand Real Food
Reflections
Physical: The APT-2D study’s finding that insulin secretion defects play a larger role in Asian populations than previously understood has direct implications for screening and early intervention worldwide. BMI cutoffs that miss visceral adiposity in lean-appearing individuals are a structural blind spot in global health policy.
Sensual: The CGM liberated rather than restricted. Discovering that foods she enjoyed were metabolically fine, while foods she was forcing herself to eat were spiking her glucose, captures something important about the relationship between pleasure and health.
Emotional: “It’s not a matter of willpower, but discipline. Your system gives you feedback. If the system is not balanced, you’re not getting the right feedback.” Reframing metabolic health as a feedback problem rather than a moral failing changes the emotional register of the entire clinical encounter.
Professional: Technology does pattern recognition, longitudinal tracking, data visualization. Humans do the contextualizing, relating, motivating. Neither replaces the other.
Sociocultural: Singapore’s policy approach, making the healthy choice the easy choice through urban design, transport policy, and food regulation, acknowledges that individual behaviour is shaped by the systems people inhabit, and that changing those systems is a legitimate form of healthcare.
Soul Inquiry
Please share your thoughts below.
International Diabetes Federation. (2024). IDF Diabetes Atlas: Singapore country profile. https://diabetesatlas.org/data-by-location/country/singapore/; Ministry of Health, Singapore. (2020). National Population Health Survey 2024.
WHO Expert Consultation. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The Lancet, 363(9403), 157-163. https://doi.org/10.1016/S0140-6736(03)15268-3
Deurenberg, P., Deurenberg-Yap, M., & Guricci, S. (2002). Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obesity Reviews, 3(3), 141-146. https://doi.org/10.1046/j.1467-789X.2002.00065.x
Ministry of Health, Singapore: National Population Health Survey 2024.
Lovejoy, J. C., Champagne, C. M., de Jonge, L., Xie, H., & Smith, S. R. (2020). Metabolomic signatures for visceral adiposity and dysglycaemia in Asian Chinese and Caucasian European adults: the cross-sectional TOFI_Asia study. Nutrition & Metabolism, 17, 87. https://doi.org/10.1186/s12986-020-00518-z
Lear, S. A., Humphries, K. H., Kohli, S., Chockalingam, A., Frohlich, J. J., & Birmingham, C. L. (2007). Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (M-CHAT). The American Journal of Clinical Nutrition, 86(2), 353-359. https://doi.org/10.1093/ajcn/86.2.353 Caleyachetty, R., Barber, T. M., Mohammed, N. I., Cappuccio, F. P., Hardy, R., Mathur, R., Banerjee, A., & Gill, P. (2021). Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. The Lancet Diabetes and Endocrinology, 9(7), 419-426. https://doi.org/10.1016/S2213-8587(21)00088-7
ClinicalTrials.gov. (n.d.). Assessing Progression to Type-2 Diabetes (APT-2D): A Prospective Cohort Study Expanded From BRITE-SP (Identifier No. NCT02838693). National Library of Medicine (U.S.). https://clinicaltrials.gov/study/NCT02838693
Magkos, F., Lee, M. H., Lim, M., Cook, A. R., Chhay, V., Loh, T. P., Chia, K. S., Baig, S., Ang, I. Y. H., Tay, J. Y. Y., Khoo, C. M., Halter, J. B., & Toh, S. A. (2022). Dynamic assessment of insulin secretion and insulin resistance in Asians with prediabetes. Metabolism, 128, 154957. https://doi.org/10.1016/j.metabol.2021.154957
Yabe, D., Seino, Y., Fukushima, M., & Seino, S. (2015). β-cell dysfunction versus insulin resistance in the pathogenesis of type 2 diabetes in East Asians. Current Diabetes Reports, 15(6), 36. https://doi.org/10.1007/s11892-015-0602-9
Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., Suez, J., Mahdi, J. A., Matot, E., Malka, G., Kosower, N., Rein, M., Zilberman-Schapira, G., Dohnalová, L., Pevsner-Fischer, M., Bikovsky, R., Halpern, Z., Elinav, E., & Segal, E. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079-1094. https://doi.org/10.1016/j.cell.2015.11.001
Berry, S. E., Valdes, A. M., Drew, D. A., Asnicar, F., Mazidi, M., Wolf, J., Capdevila, J., Hadjigeorgiou, G., Davies, R., Al Khatib, H., Bowyer, R. C. E., Mangino, M., Segata, N., Spector, T. D., & Franks, P. W. (2020). Human postprandial responses to food and potential for precision nutrition. Nature Medicine, 26(6), 964-973. https://doi.org/10.1038/s41591-020-0934-0
Lincoff, A. M., Brown-Frandsen, K., Colhoun, H. M., Deanfield, J., Emerson, S. S., Esbjerg, S., Hardt-Lindberg, S., Hovingh, G. K., Kahn, S. E., Kushner, R. F., Lingvay, I., Oral, T. K., Michelsen, M. M., Plutzky, J., Tornøe, C. W., & Ryan, D. H. (2023). Semaglutide and cardiovascular outcomes in obesity without diabetes. New England Journal of Medicine, 389(24), 2221-2232. https://doi.org/10.1056/NEJMoa2307563
Buffey, A. J., Herring, M. P., Langley, C. K., Donnelly, A. E., & Carson, B. P. (2022). The acute effects of interrupting prolonged sitting time in adults with standing and light-intensity walking on biomarkers of cardiometabolic health: A systematic review and meta-analysis. Sports Medicine, 52(8), 1765-1787. https://doi.org/10.1007/s40279-022-01649-4