Why Your Glucose Drops While You Sleep, and How to Tell a Real Low From a Sensor Artifact

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cgm glucose sleep hypoglycemia data-analysis
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If you are not taking insulin or a sulfonylurea, a continuous glucose monitor (CGM) trace that dips into the 60s while you sleep is usually one of two things: the normal nocturnal nadir of a healthy person, or a compression artifact caused by lying on the sensor. Overnight glucose in people without diabetes typically settles into a band of roughly 80 to 100 mg/dL, reaches its lowest point somewhere between 02:00 and 04:00, and then rises before waking as cortisol and growth hormone raise hepatic glucose output. A single smooth dip to 65 mg/dL at 03:00 with no symptoms is a different object than a 40-minute square-bottomed drop to 50 mg/dL that recovers vertically the moment you roll over. The rest of this page is about telling them apart in your own data, and about the situations where the low is real and needs a clinician rather than a notebook.

If you take insulin or a sulfonylurea, overnight lows are a medical matter, not an analysis exercise. Bring the data to the clinician who manages those medications. Nocturnal hypoglycemia in insulin-treated diabetes is common, frequently prolonged, and often asymptomatic because sleep blunts the counterregulatory and behavioral responses that would otherwise wake you.1

What a normal overnight glucose curve looks like

The shape matters more than any single number. After the last meal of the day clears, glucose settles onto a plateau maintained by hepatic glycogenolysis and gluconeogenesis against a low basal insulin level. In healthy physiology that plateau is flat and narrow, with overnight standard deviation usually under 10 mg/dL. The nadir sits a few hours after sleep onset, and then the curve turns upward in the pre-waking hours. That morning rise is the dawn phenomenon, driven by the overnight surge in growth hormone and the early-morning rise in cortisol, and it is present to some degree in people with and without diabetes.2

So the two most common overnight patterns people misread are both normal. A trough near 70 mg/dL at 03:00 followed by a climb to 95 mg/dL by 06:30 is the nadir plus dawn effect. A steady rise from 85 to 110 mg/dL between 04:00 and 07:00 with no preceding meal is the dawn effect alone, and it is not a “3am sugar spike” caused by something you ate.

Two things reliably push the real overnight curve down. The first is alcohol, which suppresses gluconeogenesis and can flatten or lower the nocturnal plateau for hours after the last drink. The second is prolonged or late-day exercise, which depletes muscle glycogen and raises insulin sensitivity well into the night. In a camp study of athletes with type 1 diabetes monitored by CGM, nocturnal hypoglycemia was strikingly frequent after days of heavy activity, which is the clearest demonstration of how long the exercise effect persists.3

Compression lows: the single most common false positive

If you sleep on the arm wearing the sensor, mechanical pressure on the tissue around the filament reduces local interstitial perfusion and the sensor reads low, sometimes dramatically low, while your actual blood glucose is unchanged. This is pressure-induced sensor attenuation, and it has a recognizable signature: a fast descent, a flat or slowly drifting floor lasting 20 to 90 minutes, and a recovery slope nearly as steep as the descent that returns to the pre-event baseline rather than overshooting it. Algorithms that detect these events do so precisely by looking at rate of change and the mismatch between the drop and the subsequent recovery.4

Real hypoglycemia looks different. It descends more gradually, the trough is rounded rather than square, and recovery is driven by counterregulation or carbohydrate, so it usually overshoots the pre-event level and takes longer. A practical field test costs one night: move the sensor to the contralateral arm, or sleep deliberately on the other side, and see whether the 02:00 events follow the sensor or stay with the clock. Events that follow the sensor are artifacts.

A concrete analysis recipe

Export your raw data rather than reading the app’s summary screens. Dexcom Clarity gives a CSV with Timestamp (YYYY-MM-DDThh:mm:ss) and Glucose Value (mg/dL) at five-minute intervals, with Low and High as string sentinels at the 40 and 400 mg/dL rails. FreeStyle Libre exports a CSV with a Record Type column where 0 is automatic scan-free readings and 1 is a manual scan, and you want to filter to type 0 for continuous analysis. Coerce glucose to numeric, replace the sentinels with 39 and 401 explicitly so you do not silently drop the most extreme points, and localize timestamps before slicing by hour.

import pandas as pd, numpy as np

df = pd.read_csv("clarity_export.csv", parse_dates=["Timestamp (YYYY-MM-DDThh:mm:ss)"])
df = df.rename(columns={"Timestamp (YYYY-MM-DDThh:mm:ss)": "t",
                        "Glucose Value (mg/dL)": "g"})[["t", "g"]]
df["g"] = pd.to_numeric(df["g"].replace({"Low": 39, "High": 401}), errors="coerce")
df = df.dropna().set_index("t").resample("5min").mean().interpolate(limit=2)

# assign each reading to the night that began the previous evening
df["night"] = (df.index - pd.Timedelta(hours=6)).date
night = df.between_time("00:00", "06:00")

summary = night.groupby("night")["g"].agg(
    nadir="min", mean="mean", sd="std",
    tb70=lambda s: 100 * (s < 70).mean(),
    tb54=lambda s: 100 * (s < 54).mean(),
)
# steepest 15-minute descent, in mg/dL per minute
night = night.assign(roc=night["g"].diff(3) / 15.0)
summary["max_fall"] = night.groupby("night")["roc"].min()

Read the output with the artifact signature in mind. A max_fall steeper than about −1.5 mg/dL/min during sleep, combined with a recovery that returns to within a few mg/dL of the pre-drop baseline, is nearly always compression. Interpret tb70 and tb54 as percentages of the six-hour window: 1% is roughly 3.6 minutes, which is a single reading and not an event. A useful event definition, and the one used in consensus CGM reporting, requires at least 15 consecutive minutes below threshold, with 54 mg/dL as the level that matters clinically and 70 mg/dL as the alert level.56

One thing your notebook cannot do is confirm a low with a fingerstick, and one thing a fingerstick cannot do is find these events in the first place. Self-monitoring at a handful of time points systematically misses both hyperglycemia and hypoglycemia that CGM captures, and the miss is worst overnight, when nobody is testing.7 The historical case that established this was pediatric type 1 diabetes, where CGM revealed nocturnal lows that were long, frequent, and largely invisible to conventional monitoring.8

When the pattern warrants a clinician rather than more analysis

Take the data to a physician if you see repeated overnight events below 54 mg/dL that survive the sensor-position test, if lows appear on nights with no alcohol and no unusual exercise, or if you wake with sweating, palpitations, or confusion that resolves after eating. The same applies if overnight lows are accompanied by daytime symptoms after meals, or by unexplained weight loss. The differential for genuine fasting hypoglycemia in someone not on glucose-lowering medication includes insulin-secreting tumors, adrenal and pituitary insufficiency, advanced liver disease, and medication effects, and separating those requires supervised biochemical testing (insulin, C-peptide, proinsulin, beta-hydroxybutyrate, and sulfonylurea screening drawn during a documented low) that cannot be done from CGM data alone. We would bring three or four weeks of exported traces with the artifact-suspect nights flagged, plus a log of alcohol, exercise, and last meal time, since that context is what makes the trace interpretable.

Waking at 03:00 is a weaker signal than most people assume. If your CGM shows a flat 90 mg/dL through the awakening, glucose is not the explanation, and the search should move to sleep architecture, alcohol-induced rebound arousal, or circadian timing. This is the main practical value of the overnight trace: it rules causes out as often as it rules them in.

Questions people also ask

What is a good blood sugar level before bed? For someone not on glucose-lowering medication, a pre-sleep value in the 80 to 120 mg/dL range with a flat trend arrow is unremarkable. Targets for anyone taking insulin or a sulfonylurea are individualized and belong to the prescribing clinician, because the right bedtime number depends on the medication, its duration of action, and the day’s activity.

What should blood sugar be at 3am, and what is the lowest it should go at night? In healthy physiology the 03:00 value is typically the lowest of the 24-hour cycle, often in the 70s to low 90s, and brief excursions into the high 60s are common on CGM. Sustained time below 54 mg/dL is the threshold treated as clinically significant in CGM reporting standards, and it should not appear in a person without diabetes.6

How do I keep my glucose from dropping too low overnight? The measurement-side answers are to test the sensor-position hypothesis first, then look at whether the events cluster after alcohol or after long or late exercise sessions, which is the pattern seen in athletes monitored by CGM.3 If the events are real and persist, the next step is a clinician, not a change in diet or medication made on your own.

How does a person feel when their glucose is low, and why might they sleep through it? Typical symptoms are sweating, tremor, palpitations, hunger, and difficulty concentrating, progressing to confusion. During sleep these warning signs are frequently absent or fail to cause awakening, which is why nocturnal events can run for an hour or more undetected and why the morning after (headache, unrefreshing sleep, night sweats on the sheets) is sometimes the only clue.1

What should I eat or drink when glucose is low at night? Treatment of a confirmed low is a clinical question, and the amount and form of carbohydrate depend on your medications and your history, so get the protocol from the clinician who manages them rather than from a general article. What we can say on the measurement side is to confirm with a fingerstick before acting on a nighttime CGM alarm, since the compression artifact described above is the more likely cause of a sudden overnight alert.4

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Footnotes

  1. Kate V. Allen, Brian M. Frier. Nocturnal Hypoglycemia: Clinical Manifestations and Therapeutic Strategies Toward Prevention. Endocrine Practice, 2003. https://doi.org/10.4158/ep.9.6.530 ↩ ↩2

  2. Mary F. Carroll, David S. Schade. The Dawn Phenomenon Revisited: Implications for Diabetes Therapy. Endocrine Practice, 2005. https://doi.org/10.4158/ep.11.1.55 ↩

  3. Katherine E. Iscoe, Matthew Corcoran, Michael C. Riddell. High Rates of Nocturnal Hypoglycemia in a Unique Sports Camp for Athletes with Type 1 Diabetes: Lessons Learned from Continuous Glucose Monitoring Systems. Canadian Journal of Diabetes, 2008. https://doi.org/10.1016/s1499-2671(08)23008-x ↩ ↩2

  4. Nihat Baysal, Fraser Cameron, Bruce A. Buckingham, et al. A Novel Method to Detect Pressure-Induced Sensor Attenuations (PISA) in an Artificial Pancreas. Journal of Diabetes Science and Technology, 2014. https://doi.org/10.1177/1932296814553267 ↩ ↩2

  5. Thomas W. Martens, Gregg D. Simonson, Richard M. Bergenstal. Using continuous glucose monitoring data in daily clinical practice. Cleveland Clinic Journal of Medicine, 2024. https://doi.org/10.3949/ccjm.91a.23090 ↩

  6. Timothy S. Bailey, George. Grunberger, Bruce W. Bode, et al. American Association Of Clinical Endocrinologists And American College Of Endocrinology 2016 Outpatient Glucose Monitoring Consensus Statement. Endocrine Practice, 2016. https://doi.org/10.4158/ep151124.cs ↩ ↩2

  7. Devna Mangrola, Christine Cox, Arianne S. Furman, et al. Self Blood Glucose Monitoring Underestimates Hyperglycemia And Hypoglycemia As Compared To Continuous Glucose Monitoring In Type 1 And Type 2 Diabetes. Endocrine Practice, 2018. https://doi.org/10.4158/ep-2017-0032 ↩

  8. Francine Ratner Kaufman, Juliana Austin, Aaron Neinstein, et al. Nocturnal hypoglycemia detected with the continuous glucose monitoring system in pediatric patients with type 1 diabetes. The Journal of Pediatrics, 2002. https://doi.org/10.1067/mpd.2002.129175 ↩